-------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\My Documents\PSRMLaptop\Hobby\Nuclear Latency\FPA_replication\Analysis_Appendix_log.log
  log type:  text
 opened on:   1 Nov 2024, 16:30:37

. 
. 
. ********************************************************************************************************
. * Table A1
. 
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. summ

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       ccode |      8,687    456.0957    256.5156         20        990
        year |      8,687    1984.345    16.64112       1950       2010
IdealPoint~S |      8,063    2.579328    1.073024    .001694   5.216559
IdealPoint~R |      8,061    1.560953    1.136606   .0002485   5.784511
IdealPoint~K |      8,061    2.023723    .9435749    .000095   5.462275
-------------+---------------------------------------------------------
IdealPoint~e |      8,053    1.763299    .9290043    .000622   5.542919
IdealPoint~a |      6,314    .8485806    .7185045   .0000215   3.793554
         mid |      8,687    .2883619    .4530264          0          1
 country_dem |      8,687    .4277298    .9373732  -1.769737   2.985535
        cinc |      8,687    .0033365    .0065311   2.46e-07   .0613487
-------------+---------------------------------------------------------
      ln_GNP |      8,224    22.37326    2.232872   16.40327   29.30789
      Nu_std |      8,686    .4627508    .2544519          0          1
suppor~S_std |      8,687    .1885776    .1831564          0          1
suppor~R_std |      8,687    .1154937    .1529778          0          1
support_Ch~d |      8,687    .2057002    .1287273          0          1
-------------+---------------------------------------------------------
support_UK~d |      8,687    .1445579    .1535259          0          1
support_Fr~d |      8,687    .1929968    .1788942          0          1

. 
. ********************************************************************************************************
. * Table A2
. 
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. 
. sort ccode year

. xi i.year
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

. 
. xtset ccode year

Panel variable: ccode (unbalanced)
 Time variable: year, 1950 to 2010, but with a gap
         Delta: 1 unit

. 
. 
. gen a = l.Nu_std
(200 missing values generated)

. gen b = l2.Nu_std
(399 missing values generated)

. gen Nu_std_lagged = l3.Nu_std
(595 missing values generated)

. 
. gen support_US_lagged = l3.support_US_std
(594 missing values generated)

. gen support_USSR_lagged = l3.support_USSR_std
(594 missing values generated)

. gen support_UK_lagged = l3.support_UK_std
(594 missing values generated)

. gen support_France_lagged = l3.support_France_std
(594 missing values generated)

. gen support_China_lagged = l3.support_China_std
(594 missing values generated)

. 
. 
. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_US_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_US_lagged, fe cluster(cc
> ode)
note: Nu_std_lagged omitted because of collinearity.
note: support_US_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      8,091
Group variable: ccode                           Number of groups  =        195

R-squared:                                      Obs per group:
     Within  = 0.9634                                         min =          2
     Between = 0.9997                                         avg =       41.5
     Overall = 0.9927                                         max =         58

                                                F(7,194)          =   22683.32
corr(u_i, Xb) = 0.8474                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 195 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                             Nu_std |
                                                L1. |   .9488381   .0184555    51.41   0.000      .912439    .9852372
                                                L2. |   .0253936   .0225312     1.13   0.261    -.0190438    .0698311
                                                    |
                                      Nu_std_lagged |  -.0610909   .0166499    -3.67   0.000     -.093929   -.0282528
                                  support_US_lagged |  -.0135047   .0135346    -1.00   0.320    -.0401986    .0131892
                                                    |
                c.Nu_std_lagged#c.support_US_lagged |   .1198976   .0442087     2.71   0.007     .0327062     .207089
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .0489951   .0098813     4.96   0.000     .0295065    .0684836
                                                    |
                                  support_US_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_US_lagged |  -.1322285   .0349698    -3.78   0.000    -.2011983   -.0632587
                                                    |
                                              _cons |   .0323936   .0028399    11.41   0.000     .0267925    .0379946
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .01320785
                                            sigma_e |  .02080001
                                                rho |  .28735106   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. estimates store m1

. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_US_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_US_lagged l4.support_USS
> R_std l4.support_China_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_US, fe
>  cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_US_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,003
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.9602                                         min =          1
     Between = 0.9994                                         avg =       36.7
     Overall = 0.9930                                         max =         56

                                                F(16,190)         =    9431.65
corr(u_i, Xb) = 0.8452                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 191 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                             Nu_std |
                                                L1. |   .9263944   .0215907    42.91   0.000     .8838061    .9689826
                                                L2. |   .0224863   .0223427     1.01   0.315    -.0215853    .0665579
                                                    |
                                      Nu_std_lagged |   -.071533   .0181675    -3.94   0.000    -.1073689   -.0356972
                                  support_US_lagged |  -.0313755   .0147345    -2.13   0.035    -.0604397   -.0023113
                                                    |
                c.Nu_std_lagged#c.support_US_lagged |   .1842133   .0494853     3.72   0.000     .0866021    .2818245
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .0569347   .0124458     4.57   0.000      .032385    .0814845
                                                    |
                                  support_US_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_US_lagged |  -.1941149   .0393736    -4.93   0.000    -.2717805   -.1164494
                                                    |
                                   support_USSR_std |
                                                L4. |   .0020317   .0033857     0.60   0.549    -.0046468    .0087101
                                                    |
                                  support_China_std |
                                                L4. |   .0045265   .0030828     1.47   0.144    -.0015543    .0106074
                                                    |
                                     support_UK_std |
                                                L4. |  -.0095905   .0026147    -3.67   0.000     -.014748   -.0044329
                                                    |
                                 support_France_std |
                                                L4. |   .0059865   .0017382     3.44   0.001     .0025579    .0094151
                                                    |
                                               cinc |
                                                L4. |   .4959965   .2110336     2.35   0.020     .0797268    .9122662
                                                    |
                                        country_dem |
                                                L4. |   .0001238   .0007021     0.18   0.860     -.001261    .0015087
                                                    |
                                             ln_GNP |
                                                L4. |   .0022184     .00058     3.82   0.000     .0010744    .0033624
                                                    |
                                             L4.mid |
                                                 1  |  -.0002084   .0007308    -0.29   0.776    -.0016499     .001233
                                                    |
                              IdealPointDistance_US |
                                                L4. |   .0002528   .0005626     0.45   0.654    -.0008569    .0013626
                                                    |
                                              _cons |  -.0043626   .0112049    -0.39   0.697    -.0264646    .0177394
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .01556231
                                            sigma_e |  .01988934
                                                rho |  .37973738   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. estimates store m2

. 
. esttab m1 m2 using TableA2.tex, se star(+ 0.10 * 0.05 ** 0.01) replace
(file TableA2.tex not found)
(output written to TableA2.tex)

. 
. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_US_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_US_lagged l4.support_USS
> R_std l4.support_China_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_US, fe
>  cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_US_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,003
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.9602                                         min =          1
     Between = 0.9994                                         avg =       36.7
     Overall = 0.9930                                         max =         56

                                                F(16,190)         =    9431.65
corr(u_i, Xb) = 0.8452                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 191 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                             Nu_std |
                                                L1. |   .9263944   .0215907    42.91   0.000     .8838061    .9689826
                                                L2. |   .0224863   .0223427     1.01   0.315    -.0215853    .0665579
                                                    |
                                      Nu_std_lagged |   -.071533   .0181675    -3.94   0.000    -.1073689   -.0356972
                                  support_US_lagged |  -.0313755   .0147345    -2.13   0.035    -.0604397   -.0023113
                                                    |
                c.Nu_std_lagged#c.support_US_lagged |   .1842133   .0494853     3.72   0.000     .0866021    .2818245
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .0569347   .0124458     4.57   0.000      .032385    .0814845
                                                    |
                                  support_US_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_US_lagged |  -.1941149   .0393736    -4.93   0.000    -.2717805   -.1164494
                                                    |
                                   support_USSR_std |
                                                L4. |   .0020317   .0033857     0.60   0.549    -.0046468    .0087101
                                                    |
                                  support_China_std |
                                                L4. |   .0045265   .0030828     1.47   0.144    -.0015543    .0106074
                                                    |
                                     support_UK_std |
                                                L4. |  -.0095905   .0026147    -3.67   0.000     -.014748   -.0044329
                                                    |
                                 support_France_std |
                                                L4. |   .0059865   .0017382     3.44   0.001     .0025579    .0094151
                                                    |
                                               cinc |
                                                L4. |   .4959965   .2110336     2.35   0.020     .0797268    .9122662
                                                    |
                                        country_dem |
                                                L4. |   .0001238   .0007021     0.18   0.860     -.001261    .0015087
                                                    |
                                             ln_GNP |
                                                L4. |   .0022184     .00058     3.82   0.000     .0010744    .0033624
                                                    |
                                             L4.mid |
                                                 1  |  -.0002084   .0007308    -0.29   0.776    -.0016499     .001233
                                                    |
                              IdealPointDistance_US |
                                                L4. |   .0002528   .0005626     0.45   0.654    -.0008569    .0013626
                                                    |
                                              _cons |  -.0043626   .0112049    -0.39   0.697    -.0264646    .0177394
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .01556231
                                            sigma_e |  .01988934
                                                rho |  .37973738   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. margins, dydx(support_US_lagged) at(Nu_std_lagged = (0(0.01)1)) force
note: default prediction is a function of possibly stochastic quantities other than e(b).

Average marginal effects                                 Number of obs = 7,003
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_US_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
support_US_lagged |
              _at |
               1  |  -.0313755   .0147345    -2.13   0.033    -.0602545   -.0024965
               2  |  -.0295528   .0142746    -2.07   0.038    -.0575305    -.001575
               3  |  -.0277689   .0138235    -2.01   0.045    -.0548624   -.0006753
               4  |  -.0260238   .0133811    -1.94   0.052    -.0522503    .0002027
               5  |  -.0243176   .0129475    -1.88   0.060    -.0496942    .0010591
               6  |  -.0226501   .0125228    -1.81   0.070    -.0471943    .0018941
               7  |  -.0210215    .012107    -1.74   0.083    -.0447508    .0027077
               8  |  -.0194317   .0117002    -1.66   0.097    -.0423637    .0035002
               9  |  -.0178808   .0113024    -1.58   0.114    -.0400331    .0042716
              10  |  -.0163686   .0109138    -1.50   0.134    -.0377593    .0050221
              11  |  -.0148953   .0105344    -1.41   0.157    -.0355425    .0057518
              12  |  -.0134608   .0101644    -1.32   0.185    -.0333827     .006461
              13  |  -.0120652   .0098037    -1.23   0.218    -.0312801    .0071498
              14  |  -.0107083   .0094526    -1.13   0.257     -.029235    .0078184
              15  |  -.0093903    .009111    -1.03   0.303    -.0272476     .008467
              16  |  -.0081111   .0087792    -0.92   0.356     -.025318    .0090958
              17  |  -.0068707   .0084572    -0.81   0.417    -.0234466    .0097051
              18  |  -.0056692   .0081452    -0.70   0.486    -.0216335    .0102952
              19  |  -.0045064   .0078433    -0.57   0.566     -.019879    .0108662
              20  |  -.0033825   .0075516    -0.45   0.654    -.0181835    .0114184
              21  |  -.0022974   .0072703    -0.32   0.752    -.0165471    .0119522
              22  |  -.0012512   .0069996    -0.18   0.858    -.0149701    .0124677
              23  |  -.0002437   .0067395    -0.04   0.971    -.0134529    .0129654
              24  |   .0007249   .0064902     0.11   0.911    -.0119956    .0134454
              25  |   .0016547   .0062518     0.26   0.791    -.0105987    .0139081
              26  |   .0025456   .0060246     0.42   0.673    -.0092624    .0143537
              27  |   .0033978   .0058086     0.58   0.559    -.0079869    .0147824
              28  |   .0042111   .0056039     0.75   0.452    -.0067724    .0151946
              29  |   .0049856   .0054107     0.92   0.357    -.0056192    .0155904
              30  |   .0057213    .005229     1.09   0.274    -.0045274    .0159699
              31  |   .0064181   .0050588     1.27   0.205     -.003497    .0163333
              32  |   .0070762   .0049002     1.44   0.149    -.0025281    .0166805
              33  |   .0076954   .0047532     1.62   0.105    -.0016206    .0170114
              34  |   .0082758   .0046175     1.79   0.073    -.0007743    .0173259
              35  |   .0088173   .0044931     1.96   0.050     .0000111    .0176236
              36  |   .0093201   .0043797     2.13   0.033      .000736    .0179042
              37  |    .009784   .0042772     2.29   0.022     .0014009    .0181671
              38  |   .0102091    .004185     2.44   0.015     .0020066    .0184115
              39  |   .0105953   .0041029     2.58   0.010     .0025538    .0186368
              40  |   .0109428   .0040303     2.72   0.007     .0030436     .018842
              41  |   .0112514   .0039667     2.84   0.005     .0034768     .019026
              42  |   .0115212   .0039115     2.95   0.003     .0038548    .0191876
              43  |   .0117522   .0038641     3.04   0.002     .0041787    .0193257
              44  |   .0119444   .0038238     3.12   0.002     .0044498    .0194389
              45  |   .0120977   .0037899     3.19   0.001     .0046696    .0195258
              46  |   .0122122   .0037618     3.25   0.001     .0048392    .0195852
              47  |   .0122879   .0037387     3.29   0.001     .0049602    .0196155
              48  |   .0123248   .0037199     3.31   0.001     .0050339    .0196156
              49  |   .0123228   .0037048     3.33   0.001     .0050616     .019584
              50  |    .012282   .0036927     3.33   0.001     .0050445    .0195196
              51  |   .0122024    .003683     3.31   0.001     .0049838     .019421
              52  |    .012084   .0036752     3.29   0.001     .0048807    .0192873
              53  |   .0119267   .0036687     3.25   0.001     .0047361    .0191173
              54  |   .0117307   .0036631     3.20   0.001     .0045511    .0189102
              55  |   .0114958   .0036579     3.14   0.002     .0043265    .0186651
              56  |    .011222   .0036527     3.07   0.002     .0040629    .0183811
              57  |   .0109095   .0036471     2.99   0.003     .0037612    .0180578
              58  |   .0105581    .003641     2.90   0.004     .0034219    .0176944
              59  |   .0101679    .003634     2.80   0.005     .0030454    .0172905
              60  |   .0097389    .003626     2.69   0.007     .0026321    .0168458
              61  |   .0092711   .0036168     2.56   0.010     .0021822    .0163599
              62  |   .0087644   .0036064     2.43   0.015     .0016961    .0158328
              63  |    .008219   .0035946     2.29   0.022     .0011737    .0152642
              64  |   .0076346   .0035815     2.13   0.033      .000615    .0146543
              65  |   .0070115   .0035672     1.97   0.049       .00002     .014003
              66  |   .0063496   .0035517     1.79   0.074    -.0006116    .0133107
              67  |   .0056488   .0035352     1.60   0.110    -.0012801    .0125777
              68  |   .0049092    .003518     1.40   0.163    -.0019859    .0118043
              69  |   .0041308   .0035003     1.18   0.238    -.0027296    .0109912
              70  |   .0033135   .0034824     0.95   0.341    -.0035119    .0101389
              71  |   .0024575   .0034648     0.71   0.478    -.0043334    .0092483
              72  |   .0015626   .0034478     0.45   0.650    -.0051951    .0083203
              73  |   .0006289   .0034321     0.18   0.855     -.006098    .0073557
              74  |  -.0003437   .0034182    -0.10   0.920    -.0070432    .0063559
              75  |   -.001355   .0034067    -0.40   0.691     -.008032     .005322
              76  |  -.0024052   .0033983    -0.71   0.479    -.0090658    .0042554
              77  |  -.0034942   .0033939    -1.03   0.303    -.0101461    .0031577
              78  |   -.004622   .0033941    -1.36   0.173    -.0112744    .0020303
              79  |  -.0057887   .0033999    -1.70   0.089    -.0124523     .000875
              80  |  -.0069941   .0034121    -2.05   0.040    -.0136817   -.0003066
              81  |  -.0082384   .0034315    -2.40   0.016    -.0149641   -.0015128
              82  |  -.0095216   .0034591    -2.75   0.006    -.0163013   -.0027418
              83  |  -.0108435   .0034957    -3.10   0.002    -.0176949   -.0039921
              84  |  -.0122043    .003542    -3.45   0.001    -.0191465    -.005262
              85  |  -.0136038   .0035989    -3.78   0.000    -.0206575   -.0065502
              86  |  -.0150423   .0036669    -4.10   0.000    -.0222292   -.0078553
              87  |  -.0165195   .0037466    -4.41   0.000    -.0238626   -.0091764
              88  |  -.0180355   .0038384    -4.70   0.000    -.0255587   -.0105124
              89  |  -.0195904   .0039427    -4.97   0.000    -.0273181   -.0118628
              90  |  -.0211841   .0040598    -5.22   0.000    -.0291412    -.013227
              91  |  -.0228166   .0041898    -5.45   0.000    -.0310285   -.0146048
              92  |   -.024488   .0043327    -5.65   0.000      -.03298    -.015996
              93  |  -.0261982   .0044886    -5.84   0.000    -.0349957   -.0174007
              94  |  -.0279472   .0046573    -6.00   0.000    -.0370753    -.018819
              95  |   -.029735   .0048387    -6.15   0.000    -.0392187   -.0202513
              96  |  -.0315616   .0050327    -6.27   0.000    -.0414254   -.0216978
              97  |  -.0334271   .0052389    -6.38   0.000    -.0436952    -.023159
              98  |  -.0353314   .0054573    -6.47   0.000    -.0460274   -.0246353
              99  |  -.0372745   .0056875    -6.55   0.000    -.0484218   -.0261272
             100  |  -.0392564   .0059294    -6.62   0.000    -.0508777   -.0276351
             101  |  -.0412772   .0061826    -6.68   0.000    -.0533948   -.0291595
-----------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f US Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. 
. 
. 
. xtreg Nu_std a b c.Nu_std_lagged##c.support_US_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_US_lagged l4.support_USSR_std l4.suppor
> t_China_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_US, fe cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_US_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,003
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.9602                                         min =          1
     Between = 0.9994                                         avg =       36.7
     Overall = 0.9930                                         max =         56

                                                F(16,190)         =    9431.65
corr(u_i, Xb) = 0.8452                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 191 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                                  a |   .9263944   .0215907    42.91   0.000     .8838061    .9689826
                                                  b |   .0224863   .0223427     1.01   0.315    -.0215853    .0665579
                                      Nu_std_lagged |   -.071533   .0181675    -3.94   0.000    -.1073689   -.0356972
                                  support_US_lagged |  -.0313755   .0147345    -2.13   0.035    -.0604397   -.0023113
                                                    |
                c.Nu_std_lagged#c.support_US_lagged |   .1842133   .0494853     3.72   0.000     .0866021    .2818245
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .0569347   .0124458     4.57   0.000      .032385    .0814845
                                                    |
                                  support_US_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_US_lagged |  -.1941149   .0393736    -4.93   0.000    -.2717805   -.1164494
                                                    |
                                   support_USSR_std |
                                                L4. |   .0020317   .0033857     0.60   0.549    -.0046468    .0087101
                                                    |
                                  support_China_std |
                                                L4. |   .0045265   .0030828     1.47   0.144    -.0015543    .0106074
                                                    |
                                     support_UK_std |
                                                L4. |  -.0095905   .0026147    -3.67   0.000     -.014748   -.0044329
                                                    |
                                 support_France_std |
                                                L4. |   .0059865   .0017382     3.44   0.001     .0025579    .0094151
                                                    |
                                               cinc |
                                                L4. |   .4959963   .2110336     2.35   0.020     .0797266     .912266
                                                    |
                                        country_dem |
                                                L4. |   .0001238   .0007021     0.18   0.860     -.001261    .0015087
                                                    |
                                             ln_GNP |
                                                L4. |   .0022184     .00058     3.82   0.000     .0010744    .0033624
                                                    |
                                             L4.mid |
                                                 1  |  -.0002084   .0007308    -0.29   0.776    -.0016499     .001233
                                                    |
                              IdealPointDistance_US |
                                                L4. |   .0002528   .0005626     0.45   0.654    -.0008569    .0013626
                                                    |
                                              _cons |  -.0043626   .0112049    -0.39   0.697    -.0264646    .0177394
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .01556231
                                            sigma_e |  .01988934
                                                rho |   .3797374   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. margins, dydx(support_US_lagged) at(Nu_std_lagged = (0(0.01)1))

Average marginal effects                                 Number of obs = 7,003
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_US_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
support_US_lagged |
              _at |
               1  |  -.0313755   .0147345    -2.13   0.033    -.0602545   -.0024965
               2  |  -.0295528   .0142746    -2.07   0.038    -.0575305    -.001575
               3  |  -.0277689   .0138235    -2.01   0.045    -.0548624   -.0006753
               4  |  -.0260238   .0133811    -1.94   0.052    -.0522503    .0002027
               5  |  -.0243176   .0129475    -1.88   0.060    -.0496942    .0010591
               6  |  -.0226501   .0125228    -1.81   0.070    -.0471943    .0018941
               7  |  -.0210215    .012107    -1.74   0.083    -.0447508    .0027077
               8  |  -.0194317   .0117002    -1.66   0.097    -.0423636    .0035002
               9  |  -.0178808   .0113024    -1.58   0.114    -.0400331    .0042716
              10  |  -.0163686   .0109138    -1.50   0.134    -.0377593    .0050221
              11  |  -.0148953   .0105344    -1.41   0.157    -.0355425    .0057518
              12  |  -.0134608   .0101644    -1.32   0.185    -.0333827     .006461
              13  |  -.0120652   .0098037    -1.23   0.218    -.0312801    .0071498
              14  |  -.0107083   .0094526    -1.13   0.257     -.029235    .0078184
              15  |  -.0093903    .009111    -1.03   0.303    -.0272476     .008467
              16  |  -.0081111   .0087792    -0.92   0.356     -.025318    .0090958
              17  |  -.0068707   .0084572    -0.81   0.417    -.0234466    .0097051
              18  |  -.0056692   .0081452    -0.70   0.486    -.0216335    .0102952
              19  |  -.0045064   .0078433    -0.57   0.566     -.019879    .0108662
              20  |  -.0033825   .0075516    -0.45   0.654    -.0181835    .0114184
              21  |  -.0022974   .0072703    -0.32   0.752    -.0165471    .0119522
              22  |  -.0012512   .0069996    -0.18   0.858    -.0149701    .0124677
              23  |  -.0002437   .0067395    -0.04   0.971    -.0134529    .0129654
              24  |   .0007249   .0064902     0.11   0.911    -.0119956    .0134454
              25  |   .0016547   .0062518     0.26   0.791    -.0105987    .0139081
              26  |   .0025456   .0060246     0.42   0.673    -.0092624    .0143537
              27  |   .0033978   .0058086     0.58   0.559    -.0079869    .0147824
              28  |   .0042111   .0056039     0.75   0.452    -.0067724    .0151946
              29  |   .0049856   .0054107     0.92   0.357    -.0056192    .0155904
              30  |   .0057213    .005229     1.09   0.274    -.0045274    .0159699
              31  |   .0064181   .0050588     1.27   0.205     -.003497    .0163333
              32  |   .0070762   .0049002     1.44   0.149    -.0025281    .0166805
              33  |   .0076954   .0047532     1.62   0.105    -.0016206    .0170114
              34  |   .0082758   .0046175     1.79   0.073    -.0007743    .0173259
              35  |   .0088173   .0044931     1.96   0.050     .0000111    .0176236
              36  |   .0093201   .0043797     2.13   0.033      .000736    .0179042
              37  |    .009784   .0042772     2.29   0.022     .0014009    .0181671
              38  |   .0102091    .004185     2.44   0.015     .0020066    .0184115
              39  |   .0105953   .0041029     2.58   0.010     .0025538    .0186368
              40  |   .0109428   .0040303     2.72   0.007     .0030436     .018842
              41  |   .0112514   .0039667     2.84   0.005     .0034768     .019026
              42  |   .0115212   .0039115     2.95   0.003     .0038548    .0191876
              43  |   .0117522   .0038641     3.04   0.002     .0041787    .0193257
              44  |   .0119444   .0038238     3.12   0.002     .0044498    .0194389
              45  |   .0120977   .0037899     3.19   0.001     .0046696    .0195258
              46  |   .0122122   .0037618     3.25   0.001     .0048392    .0195852
              47  |   .0122879   .0037387     3.29   0.001     .0049602    .0196155
              48  |   .0123247   .0037199     3.31   0.001     .0050339    .0196156
              49  |   .0123228   .0037048     3.33   0.001     .0050616     .019584
              50  |    .012282   .0036927     3.33   0.001     .0050445    .0195196
              51  |   .0122024    .003683     3.31   0.001     .0049838     .019421
              52  |    .012084   .0036752     3.29   0.001     .0048807    .0192873
              53  |   .0119267   .0036687     3.25   0.001     .0047361    .0191173
              54  |   .0117307   .0036631     3.20   0.001     .0045511    .0189102
              55  |   .0114958   .0036579     3.14   0.002     .0043265    .0186651
              56  |    .011222   .0036527     3.07   0.002     .0040629    .0183811
              57  |   .0109095   .0036471     2.99   0.003     .0037612    .0180577
              58  |   .0105581    .003641     2.90   0.004     .0034219    .0176944
              59  |   .0101679    .003634     2.80   0.005     .0030454    .0172905
              60  |   .0097389    .003626     2.69   0.007     .0026321    .0168458
              61  |   .0092711   .0036168     2.56   0.010     .0021822    .0163599
              62  |   .0087644   .0036064     2.43   0.015     .0016961    .0158328
              63  |   .0082189   .0035946     2.29   0.022     .0011737    .0152642
              64  |   .0076346   .0035815     2.13   0.033      .000615    .0146543
              65  |   .0070115   .0035672     1.97   0.049       .00002     .014003
              66  |   .0063496   .0035517     1.79   0.074    -.0006116    .0133107
              67  |   .0056488   .0035352     1.60   0.110    -.0012801    .0125777
              68  |   .0049092    .003518     1.40   0.163    -.0019859    .0118043
              69  |   .0041308   .0035003     1.18   0.238    -.0027296    .0109912
              70  |   .0033135   .0034824     0.95   0.341    -.0035119    .0101389
              71  |   .0024575   .0034648     0.71   0.478    -.0043334    .0092483
              72  |   .0015626   .0034478     0.45   0.650    -.0051951    .0083202
              73  |   .0006289   .0034321     0.18   0.855     -.006098    .0073557
              74  |  -.0003437   .0034182    -0.10   0.920    -.0070432    .0063559
              75  |   -.001355   .0034067    -0.40   0.691     -.008032     .005322
              76  |  -.0024052   .0033983    -0.71   0.479    -.0090658    .0042554
              77  |  -.0034942   .0033939    -1.03   0.303    -.0101461    .0031577
              78  |   -.004622   .0033941    -1.36   0.173    -.0112744    .0020303
              79  |  -.0057887   .0033999    -1.70   0.089    -.0124523     .000875
              80  |  -.0069941   .0034121    -2.05   0.040    -.0136817   -.0003066
              81  |  -.0082384   .0034315    -2.40   0.016    -.0149641   -.0015128
              82  |  -.0095216   .0034591    -2.75   0.006    -.0163013   -.0027418
              83  |  -.0108435   .0034957    -3.10   0.002    -.0176949   -.0039921
              84  |  -.0122043    .003542    -3.45   0.001    -.0191465    -.005262
              85  |  -.0136038   .0035989    -3.78   0.000    -.0206575   -.0065502
              86  |  -.0150423   .0036669    -4.10   0.000    -.0222292   -.0078553
              87  |  -.0165195   .0037466    -4.41   0.000    -.0238626   -.0091764
              88  |  -.0180355   .0038384    -4.70   0.000    -.0255587   -.0105124
              89  |  -.0195904   .0039427    -4.97   0.000    -.0273181   -.0118628
              90  |  -.0211841   .0040598    -5.22   0.000    -.0291412    -.013227
              91  |  -.0228166   .0041898    -5.45   0.000    -.0310285   -.0146048
              92  |   -.024488   .0043327    -5.65   0.000      -.03298    -.015996
              93  |  -.0261982   .0044886    -5.84   0.000    -.0349957   -.0174007
              94  |  -.0279472   .0046573    -6.00   0.000    -.0370753    -.018819
              95  |   -.029735   .0048387    -6.15   0.000    -.0392187   -.0202513
              96  |  -.0315616   .0050327    -6.27   0.000    -.0414254   -.0216978
              97  |  -.0334271   .0052389    -6.38   0.000    -.0436952    -.023159
              98  |  -.0353314   .0054573    -6.47   0.000    -.0460275   -.0246353
              99  |  -.0372745   .0056875    -6.55   0.000    -.0484218   -.0261272
             100  |  -.0392564   .0059294    -6.62   0.000    -.0508777   -.0276351
             101  |  -.0412772   .0061826    -6.68   0.000    -.0533948   -.0291595
-----------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f US Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A1
. 
. 
. ********************************************************************************************************
. *Table A3
. 
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. 
. sort ccode year

. xi i.year
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

. 
. xtset ccode year

Panel variable: ccode (unbalanced)
 Time variable: year, 1950 to 2010, but with a gap
         Delta: 1 unit

. 
. gen Nu_std_lagged = l3.Nu_std
(595 missing values generated)

. 
. gen support_US_lagged = l3.support_US_std
(594 missing values generated)

. gen support_USSR_lagged = l3.support_USSR_std
(594 missing values generated)

. gen support_UK_lagged = l3.support_UK_std
(594 missing values generated)

. gen support_France_lagged = l3.support_France_std
(594 missing values generated)

. gen support_China_lagged = l3.support_China_std
(594 missing values generated)

. 
. xtreg Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged l4.support_US_std l4.support_
> China_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_USSR, fe cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,002
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.8876                                         min =          1
     Between = 0.9937                                         avg =       36.7
     Overall = 0.9747                                         max =         56

                                                F(14,190)         =     701.93
corr(u_i, Xb) = 0.7958                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 191 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                        Nu_std_lagged |   .7067971   .0306907    23.03   0.000     .6462588    .7673354
                                  support_USSR_lagged |  -.0867027   .0631425    -1.37   0.171    -.2112532    .0378477
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |   .4030029   .2375219     1.70   0.091    -.0655158    .8715215
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .0970536   .0269663     3.60   0.000     .0438619    .1502454
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.3784664    .197187    -1.92   0.056    -.7674233    .0104904
                                                      |
                                       support_US_std |
                                                  L4. |  -.0010793   .0071867    -0.15   0.881    -.0152552    .0130967
                                                      |
                                    support_China_std |
                                                  L4. |    .015865   .0054295     2.92   0.004     .0051552    .0265747
                                                      |
                                       support_UK_std |
                                                  L4. |  -.0278856   .0064411    -4.33   0.000    -.0405908   -.0151804
                                                      |
                                   support_France_std |
                                                  L4. |    .008611   .0034506     2.50   0.013     .0018046    .0154173
                                                      |
                                                 cinc |
                                                  L4. |   1.300959   .9115623     1.43   0.155    -.4971237    3.099041
                                                      |
                                          country_dem |
                                                  L4. |  -.0011833   .0019074    -0.62   0.536    -.0049456    .0025791
                                                      |
                                               ln_GNP |
                                                  L4. |   .0069013   .0014607     4.72   0.000       .00402    .0097827
                                                      |
                                               L4.mid |
                                                   1  |  -.0003208   .0016729    -0.19   0.848    -.0036207    .0029791
                                                      |
                              IdealPointDistance_USSR |
                                                  L4. |    .000322   .0010257     0.31   0.754    -.0017012    .0023452
                                                      |
                                                _cons |  -.0309251   .0296498    -1.04   0.298    -.0894101    .0275599
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |  .04216468
                                              sigma_e |   .0334181
                                                  rho |  .61419259   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store p1

. margins, dydx(support_USSR_lagged) at(Nu_std_lagged = (0(0.01)1)) force

Average marginal effects                                 Number of obs = 7,002
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_USSR_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
support_USSR_lagged |
                _at |
                 1  |  -.0867027   .0631425    -1.37   0.170    -.2104598    .0370544
                 2  |  -.0827105   .0608403    -1.36   0.174    -.2019553    .0365342
                 3  |  -.0787941   .0585796    -1.35   0.179    -.1936079    .0360198
                 4  |  -.0749533   .0563605    -1.33   0.184    -.1854179    .0355113
                 5  |  -.0711882   .0541834    -1.31   0.189    -.1773856    .0350093
                 6  |  -.0674988   .0520483    -1.30   0.195    -.1695116    .0345141
                 7  |   -.063885   .0499556    -1.28   0.201    -.1617963    .0340262
                 8  |   -.060347   .0479055    -1.26   0.208    -.1542401    .0335461
                 9  |  -.0568847   .0458983    -1.24   0.215    -.1468436    .0330743
                10  |   -.053498   .0439342    -1.22   0.223    -.1396075    .0326114
                11  |  -.0501871   .0420137    -1.19   0.232    -.1325324    .0321582
                12  |  -.0469519   .0401371    -1.17   0.242    -.1256192    .0317155
                13  |  -.0437923    .038305    -1.14   0.253    -.1188686     .031284
                14  |  -.0407084   .0365177    -1.11   0.265    -.1122818     .030865
                15  |  -.0377003   .0347759    -1.08   0.278    -.1058599    .0304593
                16  |  -.0347678   .0330803    -1.05   0.293     -.099604    .0300684
                17  |   -.031911   .0314316    -1.02   0.310    -.0935158    .0296937
                18  |  -.0291299   .0298305    -0.98   0.329    -.0875967    .0293369
                19  |  -.0264245   .0282782    -0.93   0.350    -.0818487    .0289997
                20  |  -.0237948   .0267756    -0.89   0.374     -.076274    .0286844
                21  |  -.0212408    .025324    -0.84   0.402    -.0708749    .0283933
                22  |  -.0187625   .0239248    -0.78   0.433    -.0656543    .0281293
                23  |  -.0163599   .0225797    -0.72   0.469    -.0606154    .0278956
                24  |  -.0140329   .0212905    -0.66   0.510    -.0557616    .0276957
                25  |  -.0117817   .0200593    -0.59   0.557    -.0510973    .0275339
                26  |  -.0096062   .0188885    -0.51   0.611    -.0466269    .0274146
                27  |  -.0075063   .0177807    -0.42   0.673    -.0423558    .0273431
                28  |  -.0054822   .0167388    -0.33   0.743    -.0382896    .0273253
                29  |  -.0035337   .0157661    -0.22   0.823    -.0344347    .0273674
                30  |  -.0016609   .0148661    -0.11   0.911    -.0307979     .027476
                31  |   .0001362   .0140422     0.01   0.992     -.027386    .0276583
                32  |   .0018575    .013298     0.14   0.889    -.0242061    .0279212
                33  |   .0035032   .0126368     0.28   0.782    -.0212644    .0282708
                34  |   .0050732   .0120612     0.42   0.674    -.0185662    .0287127
                35  |   .0065675    .011573     0.57   0.570    -.0161151    .0292501
                36  |   .0079861   .0111726     0.71   0.475    -.0139118    .0298841
                37  |   .0093291    .010859     0.86   0.390    -.0119542    .0306123
                38  |   .0105963   .0106291     1.00   0.319    -.0102363    .0314289
                39  |   .0117878    .010478     1.13   0.261    -.0087486    .0323242
                40  |   .0129036   .0103991     1.24   0.215    -.0074782    .0332855
                41  |   .0139438   .0103846     1.34   0.179    -.0064096    .0342971
                42  |   .0149082   .0104255     1.43   0.153    -.0055254    .0353419
                43  |    .015797   .0105127     1.50   0.133    -.0048076    .0364016
                44  |   .0166101   .0106371     1.56   0.118    -.0042382    .0374584
                45  |   .0173474   .0107898     1.61   0.108    -.0038001     .038495
                46  |   .0180091   .0109627     1.64   0.100    -.0034773    .0394955
                47  |   .0185951   .0111484     1.67   0.095    -.0032554    .0404456
                48  |   .0191054   .0113405     1.68   0.092    -.0031215    .0413323
                49  |     .01954   .0115332     1.69   0.090    -.0030647    .0421446
                50  |   .0198989   .0117216     1.70   0.090     -.003075    .0428728
                51  |   .0201821   .0119015     1.70   0.090    -.0031444    .0435086
                52  |   .0203896   .0120692     1.69   0.091    -.0032656    .0440448
                53  |   .0205214   .0122217     1.68   0.093    -.0034327    .0444756
                54  |   .0205776   .0123565     1.67   0.096    -.0036407    .0447959
                55  |    .020558   .0124713     1.65   0.099    -.0038853    .0450013
                56  |   .0204628   .0125644     1.63   0.103    -.0041629    .0450885
                57  |   .0202918   .0126342     1.61   0.108    -.0044708    .0450544
                58  |   .0200452   .0126796     1.58   0.114    -.0048064    .0448967
                59  |   .0197228   .0126996     1.55   0.120    -.0051679    .0446136
                60  |   .0193248   .0126935     1.52   0.128     -.005554    .0442036
                61  |   .0188511   .0126607     1.49   0.137    -.0059635    .0436656
                62  |   .0183017    .012601     1.45   0.146    -.0063958    .0429991
                63  |   .0176766   .0125141     1.41   0.158    -.0068506    .0422038
                64  |   .0169758   .0124002     1.37   0.171    -.0073282    .0412797
                65  |   .0161993   .0122595     1.32   0.186    -.0078289    .0402275
                66  |   .0153471   .0120925     1.27   0.204    -.0083538     .039048
                67  |   .0144192      .0119     1.21   0.226    -.0089043    .0377427
                68  |   .0134156   .0116828     1.15   0.251    -.0094823    .0363135
                69  |   .0123363   .0114425     1.08   0.281    -.0100905    .0347631
                70  |   .0111814   .0111805     1.00   0.317    -.0107321    .0330949
                71  |   .0099507   .0108993     0.91   0.361    -.0114114    .0313129
                72  |   .0086444   .0106013     0.82   0.415    -.0121338    .0294226
                73  |   .0072623   .0102901     0.71   0.480    -.0129059    .0274306
                74  |   .0058046   .0099699     0.58   0.560     -.013736    .0253452
                75  |   .0042712   .0096459     0.44   0.658    -.0146344    .0231767
                76  |   .0026621   .0093245     0.29   0.775    -.0156136    .0209377
                77  |   .0009772   .0090136     0.11   0.914    -.0166892    .0186437
                78  |  -.0007833   .0087229    -0.09   0.928    -.0178797    .0163132
                79  |  -.0026195   .0084634    -0.31   0.757    -.0192074    .0139685
                80  |  -.0045314   .0082482    -0.55   0.583    -.0206976    .0116348
                81  |  -.0065189   .0080917    -0.81   0.420    -.0223783    .0093404
                82  |  -.0085822   .0080086    -1.07   0.284    -.0242788    .0071144
                83  |  -.0107212   .0080134    -1.34   0.181    -.0264272    .0049849
                84  |  -.0129359   .0081184    -1.59   0.111    -.0288477     .002976
                85  |  -.0152262   .0083325    -1.83   0.068    -.0315577    .0011052
                86  |  -.0175923   .0086604    -2.03   0.042    -.0345663   -.0006182
                87  |   -.020034   .0091024    -2.20   0.028    -.0378743   -.0021937
                88  |  -.0225515   .0096552    -2.34   0.020    -.0414753   -.0036276
                89  |  -.0251446   .0103131    -2.44   0.015     -.045358   -.0049312
                90  |  -.0278134   .0110691    -2.51   0.012    -.0495085   -.0061184
                91  |  -.0305579   .0119157    -2.56   0.010    -.0539122   -.0072037
                92  |  -.0333782   .0128457    -2.60   0.009    -.0585553    -.008201
                93  |  -.0362741   .0138527    -2.62   0.009     -.063425   -.0091232
                94  |  -.0392457   .0149311    -2.63   0.009    -.0685101   -.0099813
                95  |   -.042293   .0160759    -2.63   0.009    -.0738012   -.0107848
                96  |  -.0454159   .0172831    -2.63   0.009    -.0792901   -.0115418
                97  |  -.0486146   .0185491    -2.62   0.009    -.0849702   -.0122591
                98  |   -.051889   .0198711    -2.61   0.009    -.0908357   -.0129423
                99  |  -.0552391   .0212468    -2.60   0.009    -.0968821   -.0135961
               100  |  -.0586648   .0226741    -2.59   0.010    -.1031052   -.0142245
               101  |  -.0621663   .0241513    -2.57   0.010    -.1095019   -.0148307
-------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f USSR Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A2
. 
. 
. xtreg Nu_std c.Nu_std_lagged##c.support_China_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_China_lagged l4.support_US_std l4.suppor
> t_USSR_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_China, fe cluster(ccod
> e)
note: Nu_std_lagged omitted because of collinearity.
note: support_China_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      5,382
Group variable: ccode                           Number of groups  =        188

R-squared:                                      Obs per group:
     Within  = 0.8085                                         min =          1
     Between = 0.9965                                         avg =       28.6
     Overall = 0.9864                                         max =         36

                                                F(14,187)         =     331.48
corr(u_i, Xb) = 0.9120                          Prob > F          =     0.0000

                                                                          (Std. err. adjusted for 188 clusters in ccode)
------------------------------------------------------------------------------------------------------------------------
                                                       |               Robust
                                                Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------------------------------------------+----------------------------------------------------------------
                                         Nu_std_lagged |   .7972921   .0633792    12.58   0.000      .672262    .9223222
                                  support_China_lagged |   .0139209   .0287812     0.48   0.629    -.0428567    .0706985
                                                       |
                c.Nu_std_lagged#c.support_China_lagged |   .1727386   .1095143     1.58   0.116    -.0433037    .3887809
                                                       |
                                         Nu_std_lagged |          0  (omitted)
                                                       |
                       c.Nu_std_lagged#c.Nu_std_lagged |   .0210611   .0545176     0.39   0.700    -.0864876    .1286097
                                                       |
                                  support_China_lagged |          0  (omitted)
                                                       |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_China_lagged |  -.2573879   .0983705    -2.62   0.010    -.4514463   -.0633294
                                                       |
                                        support_US_std |
                                                   L4. |  -.0018031   .0057655    -0.31   0.755     -.013177    .0095708
                                                       |
                                      support_USSR_std |
                                                   L4. |    .008539   .0049657     1.72   0.087     -.001257     .018335
                                                       |
                                        support_UK_std |
                                                   L4. |   .0031871   .0045672     0.70   0.486    -.0058228     .012197
                                                       |
                                    support_France_std |
                                                   L4. |   .0008892     .00287     0.31   0.757    -.0047726     .006551
                                                       |
                                                  cinc |
                                                   L4. |   .8108134   .6583664     1.23   0.220    -.4879663    2.109593
                                                       |
                                           country_dem |
                                                   L4. |   .0038235   .0016266     2.35   0.020     .0006146    .0070324
                                                       |
                                                ln_GNP |
                                                   L4. |   .0041961   .0013288     3.16   0.002     .0015747    .0068176
                                                       |
                                                L4.mid |
                                                    1  |   .0002812   .0012676     0.22   0.825    -.0022194    .0027819
                                                       |
                              IdealPointDistance_China |
                                                   L4. |  -.0010516   .0013412    -0.78   0.434    -.0036975    .0015943
                                                       |
                                                 _cons |   .0011268   .0262244     0.04   0.966    -.0506068    .0528605
-------------------------------------------------------+----------------------------------------------------------------
                                               sigma_u |  .04403123
                                               sigma_e |  .02554794
                                                   rho |  .74813395   (fraction of variance due to u_i)
------------------------------------------------------------------------------------------------------------------------

. estimates store p2

. margins, dydx(support_China_lagged) at(Nu_std_lagged = (0(0.01)1)) force

Average marginal effects                                 Number of obs = 5,382
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_China_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
support_China_lagged |
                 _at |
                  1  |   .0139209   .0287812     0.48   0.629    -.0424892     .070331
                  2  |   .0156226   .0277873     0.56   0.574    -.0388395    .0700846
                  3  |   .0172727   .0268164     0.64   0.520    -.0352864    .0698318
                  4  |   .0188714   .0258687     0.73   0.466    -.0318303    .0695732
                  5  |   .0204186   .0249445     0.82   0.413    -.0284717     .069309
                  6  |   .0219144    .024044     0.91   0.362    -.0252111    .0690398
                  7  |   .0233586   .0231675     1.01   0.313    -.0220489    .0687661
                  8  |   .0247514   .0223152     1.11   0.267    -.0189856    .0684885
                  9  |   .0260927   .0214875     1.21   0.225    -.0160219    .0682074
                 10  |   .0273825   .0206845     1.32   0.186    -.0131583    .0679234
                 11  |   .0286209   .0199066     1.44   0.151    -.0103954    .0676372
                 12  |   .0298078   .0191543     1.56   0.120    -.0077339    .0673494
                 13  |   .0309432   .0184277     1.68   0.093    -.0051744    .0670607
                 14  |   .0320271   .0177272     1.81   0.071    -.0027176    .0667718
                 15  |   .0330595   .0170533     1.94   0.053    -.0003643    .0664833
                 16  |   .0340405   .0164062     2.07   0.038     .0018849     .066196
                 17  |     .03497   .0157863     2.22   0.027     .0040294    .0659106
                 18  |    .035848    .015194     2.36   0.018     .0060683    .0656276
                 19  |   .0366745   .0146295     2.51   0.012     .0080012    .0653478
                 20  |   .0374495   .0140932     2.66   0.008     .0098273    .0650718
                 21  |   .0381731   .0135854     2.81   0.005     .0115463       .0648
                 22  |   .0388452   .0131061     2.96   0.003     .0131577    .0645328
                 23  |   .0394658   .0126556     3.12   0.002     .0146613    .0642704
                 24  |    .040035   .0122339     3.27   0.001      .016057     .064013
                 25  |   .0405526   .0118409     3.42   0.001      .017345    .0637603
                 26  |   .0410188   .0114763     3.57   0.000     .0185257     .063512
                 27  |   .0414335     .01114     3.72   0.000     .0195996    .0632674
                 28  |   .0417968   .0108313     3.86   0.000     .0205679    .0630256
                 29  |   .0421085   .0105496     3.99   0.000     .0214318    .0627853
                 30  |   .0423688    .010294     4.12   0.000     .0221928    .0625447
                 31  |   .0425776   .0100637     4.23   0.000     .0228532     .062302
                 32  |   .0427349   .0098573     4.34   0.000      .023415    .0620548
                 33  |   .0428408   .0096734     4.43   0.000     .0238811    .0618004
                 34  |   .0428951   .0095107     4.51   0.000     .0242544    .0615358
                 35  |    .042898   .0093675     4.58   0.000     .0245381    .0612579
                 36  |   .0428494   .0092419     4.64   0.000     .0247356    .0609632
                 37  |   .0427493   .0091322     4.68   0.000     .0248505    .0606482
                 38  |   .0425978   .0090366     4.71   0.000     .0248864    .0603092
                 39  |   .0423948    .008953     4.74   0.000     .0248472    .0599424
                 40  |   .0421403   .0088797     4.75   0.000     .0247364    .0595442
                 41  |   .0418343   .0088147     4.75   0.000     .0245577    .0591109
                 42  |   .0414768   .0087563     4.74   0.000     .0243148    .0586389
                 43  |   .0410679   .0087027     4.72   0.000      .024011    .0581248
                 44  |   .0406075   .0086522     4.69   0.000     .0236496    .0575654
                 45  |   .0400956   .0086032     4.66   0.000     .0232336    .0569576
                 46  |   .0395322   .0085543     4.62   0.000     .0227661    .0562984
                 47  |   .0389174   .0085041     4.58   0.000     .0222496    .0555852
                 48  |   .0382511   .0084514     4.53   0.000     .0216867    .0548154
                 49  |   .0375333   .0083948     4.47   0.000     .0210797    .0539869
                 50  |    .036764   .0083335     4.41   0.000     .0204306    .0530974
                 51  |   .0359433   .0082664     4.35   0.000     .0197414    .0521451
                 52  |    .035071   .0081927     4.28   0.000     .0190137    .0511284
                 53  |   .0341473   .0081116     4.21   0.000      .018249    .0500457
                 54  |   .0331721   .0080223     4.13   0.000     .0174486    .0488956
                 55  |   .0321455   .0079245     4.06   0.000     .0166138    .0476772
                 56  |   .0310673   .0078175     3.97   0.000     .0157453    .0463893
                 57  |   .0299377    .007701     3.89   0.000     .0148441    .0450313
                 58  |   .0287566   .0075746     3.80   0.000     .0139107    .0436026
                 59  |    .027524   .0074382     3.70   0.000     .0129454    .0421026
                 60  |     .02624   .0072917     3.60   0.000     .0119486    .0405314
                 61  |   .0249044    .007135     3.49   0.000     .0109201    .0388888
                 62  |   .0235174   .0069684     3.37   0.001     .0098597    .0371752
                 63  |    .022079    .006792     3.25   0.001     .0087669     .035391
                 64  |    .020589   .0066064     3.12   0.002     .0076408    .0335372
                 65  |   .0190476   .0064121     2.97   0.003     .0064801     .031615
                 66  |   .0174546     .00621     2.81   0.005     .0052833     .029626
                 67  |   .0158102   .0060012     2.63   0.008      .004048    .0275725
                 68  |   .0141144   .0057873     2.44   0.015     .0027715    .0254573
                 69  |    .012367     .00557     2.22   0.026     .0014499    .0232841
                 70  |   .0105682   .0053519     1.97   0.048     .0000787    .0210577
                 71  |   .0087179   .0051358     1.70   0.090    -.0013481    .0187839
                 72  |   .0068161   .0049257     1.38   0.166     -.002838    .0164703
                 73  |   .0048628   .0047261     1.03   0.304    -.0044002    .0141259
                 74  |   .0028581   .0045429     0.63   0.529    -.0060459    .0117621
                 75  |   .0008019   .0043829     0.18   0.855    -.0077884    .0093922
                 76  |  -.0013058   .0042537    -0.31   0.759    -.0096429    .0070313
                 77  |   -.003465   .0041639    -0.83   0.405    -.0116261    .0046962
                 78  |  -.0056756   .0041219    -1.38   0.169    -.0137544    .0024032
                 79  |  -.0079377   .0041354    -1.92   0.055    -.0160429    .0001675
                 80  |  -.0102513   .0042101    -2.43   0.015    -.0185029   -.0019997
                 81  |  -.0126164   .0043492    -2.90   0.004    -.0211408   -.0040921
                 82  |   -.015033   .0045533    -3.30   0.001    -.0239572   -.0061087
                 83  |   -.017501   .0048202    -3.63   0.000    -.0269485   -.0080535
                 84  |  -.0200205   .0051464    -3.89   0.000    -.0301072   -.0099338
                 85  |  -.0225915   .0055271    -4.09   0.000    -.0334244   -.0117586
                 86  |   -.025214   .0059576    -4.23   0.000    -.0368906   -.0135374
                 87  |  -.0278879   .0064332    -4.34   0.000    -.0404968   -.0152791
                 88  |  -.0306134   .0069499    -4.40   0.000    -.0442349   -.0169918
                 89  |  -.0333903   .0075041    -4.45   0.000     -.048098   -.0186825
                 90  |  -.0362186   .0080928    -4.48   0.000    -.0520803    -.020357
                 91  |  -.0390985   .0087136    -4.49   0.000    -.0561768   -.0220201
                 92  |  -.0420298   .0093644    -4.49   0.000    -.0603837    -.023676
                 93  |  -.0450127   .0100435    -4.48   0.000    -.0646976   -.0253277
                 94  |  -.0480469   .0107496    -4.47   0.000    -.0691157   -.0269782
                 95  |  -.0511327   .0114814    -4.45   0.000    -.0736359   -.0286295
                 96  |  -.0542699   .0122382    -4.43   0.000    -.0782563   -.0302836
                 97  |  -.0574587    .013019    -4.41   0.000    -.0829754   -.0319419
                 98  |  -.0606989   .0138233    -4.39   0.000     -.087792   -.0336057
                 99  |  -.0639906   .0146505    -4.37   0.000    -.0927049   -.0352762
                100  |  -.0673337   .0155001    -4.34   0.000    -.0977133   -.0369541
                101  |  -.0707283   .0163718    -4.32   0.000    -.1028165   -.0386402
--------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f China Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A3
. 
. 
. xtreg Nu_std c.Nu_std_lagged##c.support_UK_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_UK_lagged l4.support_US_std l4.support_USSR
> _std l4.support_China_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_UK, fe cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_UK_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,004
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.8876                                         min =          1
     Between = 0.9943                                         avg =       36.7
     Overall = 0.9751                                         max =         56

                                                F(14,190)         =     968.88
corr(u_i, Xb) = 0.8044                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 191 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                      Nu_std_lagged |   .6957297   .0278603    24.97   0.000     .6407745    .7506849
                                  support_UK_lagged |   -.044537   .0517172    -0.86   0.390    -.1465507    .0574766
                                                    |
                c.Nu_std_lagged#c.support_UK_lagged |    .258667   .1986389     1.30   0.194    -.1331538    .6504878
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .1168385   .0249665     4.68   0.000     .0675914    .1660856
                                                    |
                                  support_UK_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_UK_lagged |  -.2955363   .1660512    -1.78   0.077    -.6230769    .0320043
                                                    |
                                     support_US_std |
                                                L4. |  -.0017347    .007161    -0.24   0.809      -.01586    .0123905
                                                    |
                                   support_USSR_std |
                                                L4. |   .0051956    .008514     0.61   0.542    -.0115985    .0219898
                                                    |
                                  support_China_std |
                                                L4. |   .0123834   .0053991     2.29   0.023     .0017336    .0230333
                                                    |
                                 support_France_std |
                                                L4. |   .0038406    .002999     1.28   0.202    -.0020749    .0097562
                                                    |
                                               cinc |
                                                L4. |   1.114115   .8332037     1.34   0.183    -.5294024    2.757633
                                                    |
                                        country_dem |
                                                L4. |  -.0014115   .0019401    -0.73   0.468    -.0052384    .0024153
                                                    |
                                             ln_GNP |
                                                L4. |   .0071299   .0014388     4.96   0.000     .0042918     .009968
                                                    |
                                             L4.mid |
                                                 1  |  -.0000171   .0016526    -0.01   0.992     -.003277    .0032428
                                                    |
                              IdealPointDistance_UK |
                                                L4. |  -.0000224   .0015495    -0.01   0.988    -.0030789    .0030341
                                                    |
                                              _cons |  -.0362631   .0270516    -1.34   0.182    -.0896231     .017097
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .04189801
                                            sigma_e |  .03341598
                                                rho |  .61121174   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. estimates store p3

. margins, dydx(support_UK_lagged) at(Nu_std_lagged = (0(0.01)1)) force

Average marginal effects                                 Number of obs = 7,004
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_UK_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
support_UK_lagged |
              _at |
               1  |   -.044537   .0517172    -0.86   0.389    -.1459009    .0568268
               2  |  -.0419799   .0498134    -0.84   0.399    -.1396123    .0556525
               3  |  -.0394819   .0479455    -0.82   0.410    -.1334534    .0544895
               4  |   -.037043   .0461139    -0.80   0.422    -.1274245    .0533385
               5  |  -.0346632   .0443187    -0.78   0.434    -.1215263    .0521999
               6  |  -.0323425   .0425603    -0.76   0.447    -.1157592    .0510742
               7  |   -.030081    .040839    -0.74   0.461    -.1101239     .049962
               8  |  -.0278785   .0391551    -0.71   0.476     -.104621     .048864
               9  |  -.0257351   .0375089    -0.69   0.493    -.0992512     .047781
              10  |  -.0236509   .0359009    -0.66   0.510    -.0940154    .0467137
              11  |  -.0216257   .0343317    -0.63   0.529    -.0889145    .0456631
              12  |  -.0196597   .0328016    -0.60   0.549    -.0839495    .0446302
              13  |  -.0177527   .0313112    -0.57   0.571    -.0791216    .0436162
              14  |  -.0159049   .0298613    -0.53   0.594    -.0744321    .0426223
              15  |  -.0141162   .0284526    -0.50   0.620    -.0698823    .0416499
              16  |  -.0123866   .0270859    -0.46   0.647     -.065474    .0407008
              17  |  -.0107161   .0257621    -0.42   0.677    -.0612089    .0397768
              18  |  -.0091047   .0244823    -0.37   0.710     -.057089    .0388797
              19  |  -.0075524   .0232476    -0.32   0.745    -.0531168    .0380121
              20  |  -.0060592   .0220593    -0.27   0.784    -.0492947    .0371763
              21  |  -.0046251    .020919    -0.22   0.825    -.0456255    .0363753
              22  |  -.0032501   .0198281    -0.16   0.870    -.0421125    .0356122
              23  |  -.0019343   .0187885    -0.10   0.918    -.0387591    .0348906
              24  |  -.0006775   .0178022    -0.04   0.970    -.0355693    .0342143
              25  |   .0005201   .0168713     0.03   0.975     -.032547    .0335873
              26  |   .0016587    .015998     0.10   0.917    -.0296969    .0330143
              27  |   .0027381   .0151847     0.18   0.857    -.0270234    .0324996
              28  |   .0037584   .0144338     0.26   0.795    -.0245313    .0320482
              29  |   .0047197   .0137476     0.34   0.731    -.0222251    .0316644
              30  |   .0056218   .0131282     0.43   0.668     -.020109    .0313526
              31  |   .0064648   .0125774     0.51   0.607    -.0181864     .031116
              32  |   .0072487   .0120964     0.60   0.549    -.0164598    .0309572
              33  |   .0079735   .0116856     0.68   0.495      -.01493    .0308769
              34  |   .0086392   .0113447     0.76   0.446     -.013596    .0308743
              35  |   .0092457   .0110719     0.84   0.404    -.0124549    .0309463
              36  |   .0097932   .0108647     0.90   0.367    -.0115012    .0310876
              37  |   .0102816    .010719     0.96   0.337    -.0107273    .0312905
              38  |   .0107108   .0106301     1.01   0.314    -.0101238    .0315455
              39  |    .011081   .0105923     1.05   0.295    -.0096795    .0318414
              40  |    .011392   .0105992     1.07   0.282     -.009382     .032166
              41  |   .0116439   .0106443     1.09   0.274    -.0092185    .0325064
              42  |   .0118368   .0107211     1.10   0.270    -.0091761    .0328497
              43  |   .0119705    .010823     1.11   0.269    -.0092423    .0331832
              44  |   .0120451   .0109441     1.10   0.271    -.0094049    .0334951
              45  |   .0120606   .0110786     1.09   0.276    -.0096531    .0337743
              46  |    .012017   .0112215     1.07   0.284    -.0099766    .0340106
              47  |   .0119143    .011368     1.05   0.295    -.0103665    .0341951
              48  |   .0117525   .0115141     1.02   0.307    -.0108147    .0343196
              49  |   .0115315   .0116561     0.99   0.323     -.011314    .0343771
              50  |   .0112515   .0117909     0.95   0.340    -.0118582    .0343613
              51  |   .0109124   .0119157     0.92   0.360     -.012442    .0342668
              52  |   .0105141   .0120282     0.87   0.382    -.0130607     .034089
              53  |   .0100568   .0121262     0.83   0.407    -.0137102    .0338237
              54  |   .0095403    .012208     0.78   0.435     -.014387    .0334677
              55  |   .0089647   .0122721     0.73   0.465    -.0150882    .0330177
              56  |   .0083301   .0123172     0.68   0.499    -.0158113    .0324714
              57  |   .0076363   .0123422     0.62   0.536     -.016554    .0318266
              58  |   .0068834   .0123462     0.56   0.577    -.0173148    .0310816
              59  |   .0060714   .0123285     0.49   0.622     -.018092    .0302348
              60  |   .0052003   .0122885     0.42   0.672    -.0188846    .0292852
              61  |   .0042701   .0122256     0.35   0.727    -.0196917    .0282319
              62  |   .0032808   .0121398     0.27   0.787    -.0205128    .0270743
              63  |   .0022323   .0120307     0.19   0.853    -.0213473     .025812
              64  |   .0011248   .0118983     0.09   0.925    -.0221954     .024445
              65  |  -.0000418   .0117428    -0.00   0.997    -.0230572    .0229736
              66  |  -.0012676   .0115643    -0.11   0.913    -.0239333    .0213981
              67  |  -.0025524   .0113634    -0.22   0.822    -.0248244    .0197195
              68  |  -.0038964   .0111407    -0.35   0.727    -.0257318     .017939
              69  |  -.0052995    .010897    -0.49   0.627    -.0266571    .0160582
              70  |  -.0067617   .0106333    -0.64   0.525    -.0276025    .0140792
              71  |  -.0082829   .0103511    -0.80   0.424    -.0285708    .0120049
              72  |  -.0098633   .0100523    -0.98   0.326    -.0295654    .0098388
              73  |  -.0115028    .009739    -1.18   0.238    -.0305909    .0075852
              74  |  -.0132014   .0094141    -1.40   0.161    -.0316527    .0052498
              75  |  -.0149592   .0090811    -1.65   0.100    -.0327579    .0028395
              76  |   -.016776   .0087446    -1.92   0.055     -.033915    .0003631
              77  |  -.0186519     .00841    -2.22   0.027    -.0351352   -.0021686
              78  |  -.0205869   .0080842    -2.55   0.011    -.0364316   -.0047422
              79  |  -.0225811   .0077756    -2.90   0.004     -.037821   -.0073412
              80  |  -.0246343   .0074944    -3.29   0.001    -.0393231   -.0099455
              81  |  -.0267467   .0072526    -3.69   0.000    -.0409615   -.0125319
              82  |  -.0289182   .0070636    -4.09   0.000    -.0427625   -.0150738
              83  |  -.0311487   .0069418    -4.49   0.000    -.0447544    -.017543
              84  |  -.0334384   .0069015    -4.85   0.000    -.0469652   -.0199116
              85  |  -.0357872   .0069552    -5.15   0.000    -.0494191   -.0221552
              86  |  -.0381951   .0071119    -5.37   0.000    -.0521342    -.024256
              87  |  -.0406621   .0073764    -5.51   0.000    -.0551195   -.0262046
              88  |  -.0431882   .0077488    -5.57   0.000    -.0583755   -.0280008
              89  |  -.0457734   .0082254    -5.56   0.000    -.0618949   -.0296519
              90  |  -.0484177   .0087999    -5.50   0.000    -.0656651   -.0311703
              91  |  -.0511212   .0094646    -5.40   0.000    -.0696714   -.0325709
              92  |  -.0538837   .0102117    -5.28   0.000    -.0738983   -.0338691
              93  |  -.0567053   .0110338    -5.14   0.000    -.0783313   -.0350794
              94  |  -.0595861   .0119244    -5.00   0.000    -.0829575   -.0362147
              95  |   -.062526   .0128778    -4.86   0.000    -.0877659    -.037286
              96  |  -.0655249   .0138891    -4.72   0.000     -.092747   -.0383028
              97  |   -.068583   .0149544    -4.59   0.000     -.097893   -.0392729
              98  |  -.0717002   .0160704    -4.46   0.000    -.1031976   -.0402028
              99  |  -.0748765   .0172344    -4.34   0.000    -.1086553   -.0410976
             100  |  -.0781119   .0184442    -4.24   0.000    -.1142619   -.0419619
             101  |  -.0814064   .0196979    -4.13   0.000    -.1200135   -.0427992
-----------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f UK Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A4
. 
. xtreg Nu_std c.Nu_std_lagged##c.support_France_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_France_lagged l4.support_US_std l4.supp
> ort_USSR_std l4.support_China_std l4.support_UK_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_France, fe cluster(cc
> ode)
note: Nu_std_lagged omitted because of collinearity.
note: support_France_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      6,998
Group variable: ccode                           Number of groups  =        190

R-squared:                                      Obs per group:
     Within  = 0.8884                                         min =          1
     Between = 0.9935                                         avg =       36.8
     Overall = 0.9747                                         max =         56

                                                F(14,189)         =     967.10
corr(u_i, Xb) = 0.7871                          Prob > F          =     0.0000

                                                                           (Std. err. adjusted for 190 clusters in ccode)
-------------------------------------------------------------------------------------------------------------------------
                                                        |               Robust
                                                 Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------------------------+----------------------------------------------------------------
                                          Nu_std_lagged |   .6985954   .0312597    22.35   0.000     .6369326    .7602582
                                  support_France_lagged |   .0060917   .0329402     0.18   0.853    -.0588861    .0710694
                                                        |
                c.Nu_std_lagged#c.support_France_lagged |   .1119308   .1310263     0.85   0.394     -.146531    .3703926
                                                        |
                                          Nu_std_lagged |          0  (omitted)
                                                        |
                        c.Nu_std_lagged#c.Nu_std_lagged |   .1130912   .0269268     4.20   0.000     .0599755    .1662068
                                                        |
                                  support_France_lagged |          0  (omitted)
                                                        |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_France_lagged |  -.1754935   .1124464    -1.56   0.120    -.3973048    .0463178
                                                        |
                                         support_US_std |
                                                    L4. |  -.0008481   .0073372    -0.12   0.908    -.0153214    .0136251
                                                        |
                                       support_USSR_std |
                                                    L4. |   .0112052   .0082069     1.37   0.174    -.0049838    .0273942
                                                        |
                                      support_China_std |
                                                    L4. |   .0139556   .0051736     2.70   0.008     .0037503     .024161
                                                        |
                                         support_UK_std |
                                                    L4. |  -.0191397   .0056694    -3.38   0.001     -.030323   -.0079563
                                                        |
                                                   cinc |
                                                    L4. |   1.230107   .9232377     1.33   0.184    -.5910667    3.051282
                                                        |
                                            country_dem |
                                                    L4. |  -.0011522   .0019288    -0.60   0.551    -.0049569    .0026525
                                                        |
                                                 ln_GNP |
                                                    L4. |   .0079264    .001537     5.16   0.000     .0048944    .0109583
                                                        |
                                                 L4.mid |
                                                     1  |   .0002092   .0016569     0.13   0.900    -.0030591    .0034776
                                                        |
                              IdealPointDistance_France |
                                                    L4. |  -.0037554   .0017295    -2.17   0.031    -.0071669   -.0003438
                                                        |
                                                  _cons |  -.0483689   .0279593    -1.73   0.085    -.1035214    .0067836
--------------------------------------------------------+----------------------------------------------------------------
                                                sigma_u |  .04131083
                                                sigma_e |  .03331251
                                                    rho |   .6059651   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------------------------------------

. estimates store p4

. margins, dydx(support_France_lagged) at(Nu_std_lagged = (0(0.01)1)) force

Average marginal effects                                 Number of obs = 6,998
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_France_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
support_France_lagged |
                  _at |
                   1  |   .0060917   .0329402     0.18   0.853      -.05847    .0706533
                   2  |   .0071934   .0316828     0.23   0.820    -.0549037    .0692905
                   3  |   .0082601   .0304496     0.27   0.786    -.0514201    .0679403
                   4  |   .0092916   .0292411     0.32   0.751    -.0480198    .0666031
                   5  |   .0102881   .0280572     0.37   0.714    -.0447029    .0652791
                   6  |   .0112495   .0268982     0.42   0.676      -.04147    .0639689
                   7  |   .0121757   .0257643     0.47   0.637    -.0383214    .0626728
                   8  |   .0130669   .0246558     0.53   0.596    -.0352576    .0613914
                   9  |    .013923   .0235729     0.59   0.555    -.0322791     .060125
                  10  |   .0147439    .022516     0.65   0.513    -.0293866    .0588744
                  11  |   .0155298   .0214853     0.72   0.470    -.0265806    .0576402
                  12  |   .0162806   .0204813     0.79   0.427    -.0238621    .0564232
                  13  |   .0169962   .0195044     0.87   0.384    -.0212317    .0552242
                  14  |   .0176768   .0185551     0.95   0.341    -.0186905    .0540442
                  15  |   .0183223    .017634     1.04   0.299    -.0162396    .0528842
                  16  |   .0189327   .0167416     1.13   0.258    -.0138802    .0517456
                  17  |   .0195079   .0158787     1.23   0.219    -.0116137    .0506296
                  18  |   .0200481   .0150461     1.33   0.183    -.0094416    .0495379
                  19  |   .0205532   .0142446     1.44   0.149    -.0073657    .0484722
                  20  |   .0210232   .0134754     1.56   0.119    -.0053881    .0474345
                  21  |   .0214581   .0127396     1.68   0.092     -.003511    .0464271
                  22  |   .0218579   .0120383     1.82   0.069    -.0017369    .0454526
                  23  |   .0222225   .0113732     1.95   0.051    -.0000685    .0445136
                  24  |   .0225521   .0107457     2.10   0.036      .001491    .0436133
                  25  |   .0228466   .0101575     2.25   0.024     .0029382     .042755
                  26  |    .023106   .0096105     2.40   0.016     .0042698    .0419422
                  27  |   .0233303   .0091064     2.56   0.010      .005482    .0411785
                  28  |   .0235195   .0086472     2.72   0.007     .0065713    .0404677
                  29  |   .0236736   .0082345     2.87   0.004     .0075343    .0398129
                  30  |   .0237926   .0078698     3.02   0.003      .008368    .0392172
                  31  |   .0238765   .0075543     3.16   0.002     .0090704    .0386826
                  32  |   .0239253   .0072882     3.28   0.001     .0096406      .03821
                  33  |    .023939   .0070715     3.39   0.001     .0100791    .0377989
                  34  |   .0239176   .0069029     3.46   0.001     .0103881     .037447
                  35  |   .0238611   .0067803     3.52   0.000     .0105718    .0371503
                  36  |   .0237695   .0067009     3.55   0.000      .010636    .0369029
                  37  |   .0236428   .0066607     3.55   0.000     .0105881    .0366974
                  38  |    .023481   .0066554     3.53   0.000     .0104367    .0365253
                  39  |   .0232841   .0066802     3.49   0.000     .0101911    .0363771
                  40  |   .0230521   .0067303     3.43   0.001     .0098609    .0362433
                  41  |    .022785   .0068007     3.35   0.001     .0094558    .0361142
                  42  |   .0224828   .0068869     3.26   0.001     .0089848    .0359808
                  43  |   .0221455   .0069843     3.17   0.002     .0084565    .0358345
                  44  |   .0217731   .0070892     3.07   0.002     .0078787    .0356676
                  45  |   .0213657   .0071978     2.97   0.003     .0072582    .0354731
                  46  |   .0209231   .0073071     2.86   0.004     .0066014    .0352448
                  47  |   .0204454   .0074143     2.76   0.006     .0059135    .0349772
                  48  |   .0199326   .0075171     2.65   0.008     .0051994    .0346658
                  49  |   .0193847   .0076132     2.55   0.011      .004463    .0343064
                  50  |   .0188017    .007701     2.44   0.015      .003708    .0338955
                  51  |   .0181837   .0077789     2.34   0.019     .0029373    .0334301
                  52  |   .0175305   .0078456     2.23   0.025     .0021534    .0329076
                  53  |   .0168422   .0078999     2.13   0.033     .0013587    .0323257
                  54  |   .0161188   .0079409     2.03   0.042      .000555    .0316827
                  55  |   .0153604   .0079678     1.93   0.054    -.0002562    .0309769
                  56  |   .0145668   .0079798     1.83   0.068    -.0010734     .030207
                  57  |   .0137381   .0079765     1.72   0.085    -.0018956    .0293718
                  58  |   .0128744   .0079574     1.62   0.106    -.0027218    .0284705
                  59  |   .0119755    .007922     1.51   0.131    -.0035513    .0275023
                  60  |   .0110415   .0078701     1.40   0.161    -.0043836    .0264666
                  61  |   .0100725   .0078015     1.29   0.197    -.0052182    .0253631
                  62  |   .0090683   .0077161     1.18   0.240     -.006055    .0241916
                  63  |    .008029   .0076138     1.05   0.292    -.0068938    .0229518
                  64  |   .0069547   .0074947     0.93   0.353    -.0077347    .0216441
                  65  |   .0058452    .007359     0.79   0.427    -.0085781    .0202685
                  66  |   .0047007   .0072068     0.65   0.514    -.0094243    .0188256
                  67  |    .003521   .0070385     0.50   0.617    -.0102742    .0173162
                  68  |   .0023062   .0068547     0.34   0.737    -.0111288    .0157412
                  69  |   .0010564   .0066561     0.16   0.874    -.0119892     .014102
                  70  |  -.0002286   .0064434    -0.04   0.972    -.0128575    .0124003
                  71  |  -.0015486    .006218    -0.25   0.803    -.0137357    .0106385
                  72  |  -.0029038   .0059813    -0.49   0.627     -.014627    .0088194
                  73  |   -.004294   .0057353    -0.75   0.454     -.015535    .0069469
                  74  |  -.0057194   .0054824    -1.04   0.297    -.0164648     .005026
                  75  |  -.0071798    .005226    -1.37   0.169    -.0174227    .0030631
                  76  |  -.0086754   .0049703    -1.75   0.081     -.018417    .0010663
                  77  |   -.010206   .0047208    -2.16   0.031    -.0194586   -.0009534
                  78  |  -.0117717   .0044844    -2.63   0.009     -.020561   -.0029825
                  79  |  -.0133726   .0042699    -3.13   0.002    -.0217415   -.0050036
                  80  |  -.0150085   .0040883    -3.67   0.000    -.0230214   -.0069957
                  81  |  -.0166796   .0039518    -4.22   0.000    -.0244249   -.0089342
                  82  |  -.0183857   .0038738    -4.75   0.000    -.0259782   -.0107932
                  83  |  -.0201269   .0038668    -5.21   0.000    -.0277056   -.0125482
                  84  |  -.0219033   .0039401    -5.56   0.000    -.0296258   -.0141807
                  85  |  -.0237147   .0040988    -5.79   0.000    -.0317482   -.0156812
                  86  |  -.0255613   .0043423    -5.89   0.000     -.034072   -.0170505
                  87  |  -.0274429    .004666    -5.88   0.000    -.0365882   -.0182976
                  88  |  -.0293596   .0050629    -5.80   0.000    -.0392828   -.0194364
                  89  |  -.0313114   .0055251    -5.67   0.000    -.0421404   -.0204824
                  90  |  -.0332984    .006045    -5.51   0.000    -.0451463   -.0214504
                  91  |  -.0353204    .006616    -5.34   0.000    -.0482876   -.0223532
                  92  |  -.0373775   .0072328    -5.17   0.000    -.0515535   -.0232015
                  93  |  -.0394697   .0078908    -5.00   0.000    -.0549354   -.0240041
                  94  |  -.0415971   .0085865    -4.84   0.000    -.0584262   -.0247679
                  95  |  -.0437595   .0093171    -4.70   0.000    -.0620206   -.0254983
                  96  |   -.045957   .0100804    -4.56   0.000    -.0657142   -.0261998
                  97  |  -.0481896   .0108746    -4.43   0.000    -.0695034   -.0268758
                  98  |  -.0504573   .0116983    -4.31   0.000    -.0733856    -.027529
                  99  |  -.0527602   .0125504    -4.20   0.000    -.0773585   -.0281618
                 100  |  -.0550981     .01343    -4.10   0.000    -.0814203   -.0287758
                 101  |  -.0574711   .0143362    -4.01   0.000    -.0855695   -.0293727
---------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f French Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A5
. 
. esttab p1 p2 p3 p4 using TableA3.tex, se star(+ 0.10 * 0.05 ** 0.01) replace
(file TableA3.tex not found)
(output written to TableA3.tex)

. 
. 
. ********************************************************************************************************
. *Table A4
. 
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. 
. sort ccode year

. xi i.year
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

. 
. xtset ccode year

Panel variable: ccode (unbalanced)
 Time variable: year, 1950 to 2010, but with a gap
         Delta: 1 unit

. 
. gen Nu_std_lagged = l3.Nu_std
(595 missing values generated)

. 
. gen support_US_lagged = l3.support_US_std
(594 missing values generated)

. gen support_USSR_lagged = l3.support_USSR_std
(594 missing values generated)

. gen support_UK_lagged = l3.support_UK_std
(594 missing values generated)

. gen support_France_lagged = l3.support_France_std
(594 missing values generated)

. gen support_China_lagged = l3.support_China_std
(594 missing values generated)

. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged, fe cluste
> r(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      8,091
Group variable: ccode                           Number of groups  =        195

R-squared:                                      Obs per group:
     Within  = 0.9633                                         min =          2
     Between = 0.9997                                         avg =       41.5
     Overall = 0.9927                                         max =         58

                                                F(7,194)          =   21566.40
corr(u_i, Xb) = 0.8485                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 195 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                               Nu_std |
                                                  L1. |   .9513544   .0184327    51.61   0.000     .9150002    .9877086
                                                  L2. |   .0259037   .0226517     1.14   0.254    -.0187715    .0705788
                                                      |
                                        Nu_std_lagged |   -.045088   .0165553    -2.72   0.007    -.0777394   -.0124365
                                  support_USSR_lagged |   .0057081   .0260857     0.22   0.827    -.0457399     .057156
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |   .0365707   .0918488     0.40   0.691    -.1445797    .2177211
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .0263265   .0094776     2.78   0.006      .007634    .0450189
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.0483187   .0737841    -0.65   0.513    -.1938407    .0972032
                                                      |
                                                _cons |   .0294664    .002872    10.26   0.000     .0238019    .0351308
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |  .01283661
                                              sigma_e |  .02083014
                                                  rho |  .27523956   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store k1

. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_China_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_China_lagged, fe clus
> ter(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_China_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      8,091
Group variable: ccode                           Number of groups  =        195

R-squared:                                      Obs per group:
     Within  = 0.9635                                         min =          2
     Between = 0.9996                                         avg =       41.5
     Overall = 0.9926                                         max =         58

                                                F(7,194)          =   18272.65
corr(u_i, Xb) = 0.8588                          Prob > F          =     0.0000

                                                                          (Std. err. adjusted for 195 clusters in ccode)
------------------------------------------------------------------------------------------------------------------------
                                                       |               Robust
                                                Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------------------------------------------+----------------------------------------------------------------
                                                Nu_std |
                                                   L1. |   .9468493   .0185476    51.05   0.000     .9102686    .9834301
                                                   L2. |   .0254502   .0221843     1.15   0.253    -.0183033    .0692036
                                                       |
                                         Nu_std_lagged |  -.0529273   .0155785    -3.40   0.001    -.0836523   -.0222023
                                  support_China_lagged |   .0300442    .016569     1.81   0.071    -.0026343    .0627227
                                                       |
                c.Nu_std_lagged#c.support_China_lagged |   .0182411   .0601168     0.30   0.762    -.1003253    .1368076
                                                       |
                                         Nu_std_lagged |          0  (omitted)
                                                       |
                       c.Nu_std_lagged#c.Nu_std_lagged |   .0407653   .0102591     3.97   0.000     .0205316     .060999
                                                       |
                                  support_China_lagged |          0  (omitted)
                                                       |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_China_lagged |  -.0720173   .0518131    -1.39   0.166    -.1742067     .030172
                                                       |
                                                 _cons |   .0291777    .003057     9.54   0.000     .0231485    .0352069
-------------------------------------------------------+----------------------------------------------------------------
                                               sigma_u |  .01544767
                                               sigma_e |  .02077421
                                                   rho |  .35605978   (fraction of variance due to u_i)
------------------------------------------------------------------------------------------------------------------------

. estimates store k2

. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_UK_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_UK_lagged, fe cluster(cc
> ode)
note: Nu_std_lagged omitted because of collinearity.
note: support_UK_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      8,091
Group variable: ccode                           Number of groups  =        195

R-squared:                                      Obs per group:
     Within  = 0.9633                                         min =          2
     Between = 0.9997                                         avg =       41.5
     Overall = 0.9927                                         max =         58

                                                F(7,194)          =   30753.71
corr(u_i, Xb) = 0.8552                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 195 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                             Nu_std |
                                                L1. |   .9508431   .0188664    50.40   0.000     .9136336    .9880527
                                                L2. |   .0258987   .0226672     1.14   0.255     -.018807    .0706045
                                                    |
                                      Nu_std_lagged |  -.0536521   .0159022    -3.37   0.001    -.0850156   -.0222886
                                  support_UK_lagged |  -.0183578   .0153874    -1.19   0.234    -.0487059    .0119902
                                                    |
                c.Nu_std_lagged#c.support_UK_lagged |   .0927428   .0604371     1.53   0.127    -.0264553    .2119408
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .0368412   .0087244     4.22   0.000     .0196343    .0540481
                                                    |
                                  support_UK_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_UK_lagged |  -.0974885   .0505679    -1.93   0.055    -.1972218    .0022449
                                                    |
                                              _cons |   .0322658   .0027434    11.76   0.000     .0268552    .0376765
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .01334101
                                            sigma_e |  .02082802
                                                rho |  .29092149   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. estimates store k3

. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_France_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_France_lagged, fe cl
> uster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_France_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      8,091
Group variable: ccode                           Number of groups  =        195

R-squared:                                      Obs per group:
     Within  = 0.9634                                         min =          2
     Between = 0.9997                                         avg =       41.5
     Overall = 0.9927                                         max =         58

                                                F(7,194)          =   29521.80
corr(u_i, Xb) = 0.8556                          Prob > F          =     0.0000

                                                                           (Std. err. adjusted for 195 clusters in ccode)
-------------------------------------------------------------------------------------------------------------------------
                                                        |               Robust
                                                 Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------------------------+----------------------------------------------------------------
                                                 Nu_std |
                                                    L1. |   .9500442   .0185464    51.23   0.000     .9134658    .9866227
                                                    L2. |   .0257542   .0226657     1.14   0.257    -.0189486     .070457
                                                        |
                                          Nu_std_lagged |  -.0512815   .0160565    -3.19   0.002    -.0829493   -.0196138
                                  support_France_lagged |   .0052085   .0098237     0.53   0.597    -.0141664    .0245834
                                                        |
                c.Nu_std_lagged#c.support_France_lagged |   .0404159   .0403025     1.00   0.317    -.0390714    .1199032
                                                        |
                                          Nu_std_lagged |          0  (omitted)
                                                        |
                        c.Nu_std_lagged#c.Nu_std_lagged |   .0357336   .0086353     4.14   0.000     .0187026    .0527647
                                                        |
                                  support_France_lagged |          0  (omitted)
                                                        |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_France_lagged |  -.0628582    .034777    -1.81   0.072    -.1314476    .0057313
                                                        |
                                                  _cons |   .0305886   .0027181    11.25   0.000     .0252277    .0359495
--------------------------------------------------------+----------------------------------------------------------------
                                                sigma_u |  .01370111
                                                sigma_e |  .02082141
                                                    rho |  .30216482   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------------------------------------

. estimates store k4

. 
. 
. esttab k1 k2 k3 k4 using TableA4.tex, se star(+ 0.10 * 0.05 ** 0.01) replace
(file TableA4.tex not found)
(output written to TableA4.tex)

. 
. 
. ********************************************************************************************************
. *TableA5
. 
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. 
. sort ccode year

. xi i.year
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

. 
. xtset ccode year

Panel variable: ccode (unbalanced)
 Time variable: year, 1950 to 2010, but with a gap
         Delta: 1 unit

. 
. gen Nu_std_lagged = l3.Nu_std
(595 missing values generated)

. 
. gen support_US_lagged = l3.support_US_std
(594 missing values generated)

. gen support_USSR_lagged = l3.support_USSR_std
(594 missing values generated)

. gen support_UK_lagged = l3.support_UK_std
(594 missing values generated)

. gen support_France_lagged = l3.support_France_std
(594 missing values generated)

. gen support_China_lagged = l3.support_China_std
(594 missing values generated)

. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged l4.support
> _US_std l4.support_China_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_USSR
> , fe cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,001
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.9599                                         min =          1
     Between = 0.9994                                         avg =       36.7
     Overall = 0.9930                                         max =         56

                                                F(16,190)         =    4926.99
corr(u_i, Xb) = 0.8332                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 191 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                               Nu_std |
                                                  L1. |   .9338628   .0216245    43.19   0.000     .8912078    .9765178
                                                  L2. |   .0220869   .0225338     0.98   0.328    -.0223617    .0665356
                                                      |
                                        Nu_std_lagged |  -.0449587   .0177725    -2.53   0.012    -.0800155   -.0099019
                                  support_USSR_lagged |   .0170963   .0278731     0.61   0.540    -.0378842    .0720767
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |  -.0145678   .1049375    -0.14   0.890      -.22156    .1924245
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .0206401   .0116123     1.78   0.077    -.0022655    .0435458
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.0133222   .0869666    -0.15   0.878    -.1848663    .1582219
                                                      |
                                       support_US_std |
                                                  L4. |   .0007661   .0029278     0.26   0.794     -.005009    .0065412
                                                      |
                                    support_China_std |
                                                  L4. |   .0054406   .0030703     1.77   0.078    -.0006157    .0114969
                                                      |
                                       support_UK_std |
                                                  L4. |  -.0124277   .0026897    -4.62   0.000    -.0177333   -.0071221
                                                      |
                                   support_France_std |
                                                  L4. |   .0055969   .0017535     3.19   0.002     .0021381    .0090556
                                                      |
                                                 cinc |
                                                  L4. |   .4133238   .2941175     1.41   0.162    -.1668313    .9934789
                                                      |
                                          country_dem |
                                                  L4. |   .0003521   .0007341     0.48   0.632    -.0010959    .0018001
                                                      |
                                               ln_GNP |
                                                  L4. |   .0022997   .0005634     4.08   0.000     .0011884     .003411
                                                      |
                                               L4.mid |
                                                   1  |  -.0002216   .0007394    -0.30   0.765    -.0016801     .001237
                                                      |
                              IdealPointDistance_USSR |
                                                  L4. |   .0004209   .0003836     1.10   0.274    -.0003358    .0011775
                                                      |
                                                _cons |  -.0121139   .0116021    -1.04   0.298    -.0349994    .0107715
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |   .0150642
                                              sigma_e |  .01995152
                                                  rho |   .3630922   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store p1

. margins, dydx(support_USSR_lagged) at(Nu_std_lagged = (0(0.01)1)) force
note: default prediction is a function of possibly stochastic quantities other than e(b).

Average marginal effects                                 Number of obs = 7,001
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_USSR_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
support_USSR_lagged |
                _at |
                 1  |   .0170963   .0278731     0.61   0.540     -.037534    .0717265
                 2  |   .0169493   .0268496     0.63   0.528    -.0356749    .0695734
                 3  |   .0167996   .0258442     0.65   0.516     -.033854    .0674532
                 4  |   .0166472   .0248569     0.67   0.503    -.0320714    .0653658
                 5  |   .0164922   .0238878     0.69   0.490     -.030327    .0633115
                 6  |   .0163346    .022937     0.71   0.476    -.0286212    .0612904
                 7  |   .0161742   .0220046     0.74   0.462     -.026954    .0593025
                 8  |   .0160112   .0210907     0.76   0.448    -.0253257    .0573482
                 9  |   .0158456   .0201952     0.78   0.433    -.0237363    .0554275
                10  |   .0156773   .0193184     0.81   0.417    -.0221862    .0535407
                11  |   .0155063   .0184604     0.84   0.401    -.0206755    .0516881
                12  |   .0153326   .0176214     0.87   0.384    -.0192046    .0498698
                13  |   .0151563   .0168013     0.90   0.367    -.0177737    .0480863
                14  |   .0149773   .0160006     0.94   0.349    -.0163832    .0463378
                15  |   .0147957   .0152192     0.97   0.331    -.0150335    .0446248
                16  |   .0146114   .0144576     1.01   0.312     -.013725    .0429477
                17  |   .0144244   .0137159     1.05   0.293    -.0124583     .041307
                18  |   .0142347   .0129944     1.10   0.273    -.0112339    .0397033
                19  |   .0140424   .0122935     1.14   0.253    -.0100525    .0381373
                20  |   .0138475   .0116137     1.19   0.233    -.0089149    .0366098
                21  |   .0136498   .0109553     1.25   0.213    -.0078221    .0351217
                22  |   .0134495   .0103188     1.30   0.192     -.006775    .0336741
                23  |   .0132466   .0097051     1.36   0.172     -.005775    .0322681
                24  |   .0130409   .0091146     1.43   0.152    -.0048234    .0309053
                25  |   .0128326   .0085484     1.50   0.133     -.003922    .0295873
                26  |   .0126217   .0080074     1.58   0.115    -.0030726    .0283159
                27  |   .0124081   .0074928     1.66   0.098    -.0022775    .0270936
                28  |   .0121918   .0070058     1.74   0.082    -.0015394    .0259229
                29  |   .0119728   .0065481     1.83   0.067    -.0008612    .0248068
                30  |   .0117512   .0061213     1.92   0.055    -.0002463    .0237487
                31  |   .0115269   .0057273     2.01   0.044     .0003015    .0227523
                32  |      .0113   .0053683     2.10   0.035     .0007783    .0218217
                33  |   .0110704   .0050464     2.19   0.028     .0011796    .0209611
                34  |   .0108381   .0047636     2.28   0.023     .0015017    .0201746
                35  |   .0106032   .0045217     2.34   0.019     .0017408    .0194656
                36  |   .0103656   .0043221     2.40   0.016     .0018944    .0188368
                37  |   .0101253   .0041653     2.43   0.015     .0019616    .0182891
                38  |   .0098824   .0040506     2.44   0.015     .0019433    .0178214
                39  |   .0096368   .0039764     2.42   0.015     .0018431    .0174305
                40  |   .0093885   .0039399     2.38   0.017     .0016665    .0171105
                41  |   .0091376   .0039369     2.32   0.020     .0014213    .0168539
                42  |    .008884   .0039631     2.24   0.025     .0011166    .0166515
                43  |   .0086278   .0040132     2.15   0.032     .0007621    .0164935
                44  |   .0083689   .0040824     2.05   0.040     .0003676    .0163701
                45  |   .0081073   .0041658     1.95   0.052    -.0000575    .0162721
                46  |    .007843   .0042591     1.84   0.066    -.0005047    .0161907
                47  |   .0075761   .0043585     1.74   0.082    -.0009663    .0161185
                48  |   .0073065   .0044604     1.64   0.101    -.0014358    .0160489
                49  |   .0070343   .0045622     1.54   0.123    -.0019075    .0159761
                50  |   .0067594   .0046613     1.45   0.147    -.0023767    .0158955
                51  |   .0064818   .0047558     1.36   0.173    -.0028393     .015803
                52  |   .0062016   .0048438     1.28   0.200    -.0032921    .0156953
                53  |   .0059187    .004924     1.20   0.229    -.0037322    .0155696
                54  |   .0056331   .0049953     1.13   0.259    -.0041574    .0154237
                55  |   .0053449   .0050565     1.06   0.290    -.0045657    .0152555
                56  |    .005054    .005107     0.99   0.322    -.0049555    .0150636
                57  |   .0047605    .005146     0.93   0.355    -.0053256    .0148465
                58  |   .0044643   .0051731     0.86   0.388    -.0056748    .0146034
                59  |   .0041654   .0051877     0.80   0.422    -.0060024    .0143332
                60  |   .0038638   .0051896     0.74   0.457    -.0063076    .0140353
                61  |   .0035596   .0051785     0.69   0.492      -.00659    .0137092
                62  |   .0032527   .0051541     0.63   0.528    -.0068491    .0133546
                63  |   .0029432   .0051164     0.58   0.565    -.0070849    .0129713
                64  |    .002631   .0050654     0.52   0.603    -.0072971    .0125591
                65  |   .0023161   .0050011     0.46   0.643    -.0074858    .0121181
                66  |   .0019986   .0049235     0.41   0.685    -.0076514    .0116485
                67  |   .0016784    .004833     0.35   0.728    -.0077941    .0111508
                68  |   .0013555   .0047297     0.29   0.774    -.0079145    .0106256
                69  |     .00103   .0046141     0.22   0.823    -.0080135    .0100735
                70  |   .0007018   .0044868     0.16   0.876    -.0080921    .0094957
                71  |    .000371   .0043484     0.09   0.932    -.0081518    .0088937
                72  |   .0000374      .0042     0.01   0.993    -.0081944    .0082693
                73  |  -.0002987   .0040427    -0.07   0.941    -.0082224    .0076249
                74  |  -.0006376   .0038782    -0.16   0.869    -.0082388    .0069636
                75  |  -.0009791   .0037086    -0.26   0.792    -.0082478    .0062896
                76  |  -.0013233   .0035365    -0.37   0.708    -.0082547    .0056081
                77  |  -.0016701   .0033655    -0.50   0.620    -.0082664    .0049261
                78  |  -.0020196   .0032001    -0.63   0.528    -.0082917    .0042525
                79  |  -.0023718   .0030462    -0.78   0.436    -.0083422    .0035986
                80  |  -.0027266    .002911    -0.94   0.349     -.008432    .0029787
                81  |  -.0030841   .0028032    -1.10   0.271    -.0085782    .0024099
                82  |  -.0034443   .0027325    -1.26   0.207    -.0087999    .0019112
                83  |  -.0038071   .0027087    -1.41   0.160    -.0091161    .0015018
                84  |  -.0041726   .0027401    -1.52   0.128    -.0095432    .0011979
                85  |  -.0045408   .0028321    -1.60   0.109    -.0100917    .0010101
                86  |  -.0049116   .0029863    -1.64   0.100    -.0107646    .0009414
                87  |  -.0052851   .0032005    -1.65   0.099    -.0115579    .0009877
                88  |  -.0056613   .0034703    -1.63   0.103    -.0124629    .0011404
                89  |  -.0060401   .0037902    -1.59   0.111    -.0134686    .0013885
                90  |  -.0064215   .0041545    -1.55   0.122    -.0145642    .0017211
                91  |  -.0068057   .0045584    -1.49   0.135    -.0157399    .0021285
                92  |  -.0071925   .0049975    -1.44   0.150    -.0169874    .0026024
                93  |   -.007582   .0054684    -1.39   0.166    -.0182999    .0031359
                94  |  -.0079741   .0059684    -1.34   0.182     -.019672    .0037238
                95  |  -.0083689   .0064953    -1.29   0.198    -.0210995    .0043617
                96  |  -.0087664   .0070474    -1.24   0.214     -.022579    .0050462
                97  |  -.0091665   .0076232    -1.20   0.229    -.0241078    .0057747
                98  |  -.0095693   .0082218    -1.16   0.244    -.0256838    .0065451
                99  |  -.0099748   .0088422    -1.13   0.259    -.0273052    .0073556
               100  |  -.0103829   .0094838    -1.09   0.274    -.0289707    .0082049
               101  |  -.0107937   .0101459    -1.06   0.287    -.0306792    .0090918
-------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f USSR Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A6
. 
. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_China_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_China_lagged l4.suppo
> rt_US_std l4.support_USSR_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_Chi
> na, fe cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_China_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      5,382
Group variable: ccode                           Number of groups  =        188

R-squared:                                      Obs per group:
     Within  = 0.9309                                         min =          1
     Between = 0.9997                                         avg =       28.6
     Overall = 0.9962                                         max =         36

                                                F(16,187)         =    2397.09
corr(u_i, Xb) = 0.9370                          Prob > F          =     0.0000

                                                                          (Std. err. adjusted for 188 clusters in ccode)
------------------------------------------------------------------------------------------------------------------------
                                                       |               Robust
                                                Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------------------------------------------+----------------------------------------------------------------
                                                Nu_std |
                                                   L1. |   .9459497   .0288593    32.78   0.000      .889018    1.002881
                                                   L2. |   .0150709   .0286007     0.53   0.599    -.0413507    .0714924
                                                       |
                                         Nu_std_lagged |  -.0190134   .0240881    -0.79   0.431    -.0665328    .0285059
                                  support_China_lagged |   .0162127   .0170157     0.95   0.342    -.0173546    .0497801
                                                       |
                c.Nu_std_lagged#c.support_China_lagged |   .0353467   .0638565     0.55   0.581    -.0906249    .1613184
                                                       |
                                         Nu_std_lagged |          0  (omitted)
                                                       |
                       c.Nu_std_lagged#c.Nu_std_lagged |  -.0078164   .0191819    -0.41   0.684    -.0456572    .0300244
                                                       |
                                  support_China_lagged |          0  (omitted)
                                                       |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_China_lagged |  -.0730016   .0556505    -1.31   0.191    -.1827851    .0367819
                                                       |
                                        support_US_std |
                                                   L4. |   .0002138   .0025147     0.09   0.932     -.004747    .0051747
                                                       |
                                      support_USSR_std |
                                                   L4. |   .0040609   .0020949     1.94   0.054    -.0000718    .0081936
                                                       |
                                        support_UK_std |
                                                   L4. |  -.0027746   .0021783    -1.27   0.204    -.0070718    .0015227
                                                       |
                                    support_France_std |
                                                   L4. |    .002497   .0016088     1.55   0.122    -.0006767    .0056706
                                                       |
                                                  cinc |
                                                   L4. |   .2478286   .2473412     1.00   0.318     -.240109    .7357662
                                                       |
                                           country_dem |
                                                   L4. |   .0019322   .0005847     3.30   0.001     .0007788    .0030856
                                                       |
                                                ln_GNP |
                                                   L4. |   .0012301   .0004777     2.58   0.011     .0002878    .0021724
                                                       |
                                                L4.mid |
                                                    1  |   .0004371   .0006126     0.71   0.476    -.0007715    .0016456
                                                       |
                              IdealPointDistance_China |
                                                   L4. |  -.0008088   .0005244    -1.54   0.125    -.0018434    .0002257
                                                       |
                                                 _cons |   .0032279   .0097704     0.33   0.741    -.0160465    .0225023
-------------------------------------------------------+----------------------------------------------------------------
                                               sigma_u |  .01638564
                                               sigma_e |   .0153493
                                                   rho |  .53262148   (fraction of variance due to u_i)
------------------------------------------------------------------------------------------------------------------------

. estimates store p2

. margins, dydx(support_China_lagged) at(Nu_std_lagged = (0(0.01)1)) force
note: default prediction is a function of possibly stochastic quantities other than e(b).

Average marginal effects                                 Number of obs = 5,382
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_China_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
support_China_lagged |
                 _at |
                  1  |   .0162127   .0170157     0.95   0.341    -.0171374    .0495629
                  2  |   .0165589   .0164285     1.01   0.313    -.0156404    .0487583
                  3  |   .0168905   .0158542     1.07   0.287    -.0141833    .0479642
                  4  |   .0172074   .0152929     1.13   0.261    -.0127661     .047181
                  5  |   .0175098   .0147446     1.19   0.235    -.0113892    .0464088
                  6  |   .0177976   .0142096     1.25   0.210    -.0100527    .0456479
                  7  |   .0180707   .0136879     1.32   0.187     -.008757    .0448985
                  8  |   .0183293   .0131796     1.39   0.164    -.0075023    .0441609
                  9  |   .0185733   .0126851     1.46   0.143     -.006289    .0434355
                 10  |   .0188026   .0122043     1.54   0.123    -.0051174    .0427226
                 11  |   .0190174   .0117375     1.62   0.105    -.0039877    .0420225
                 12  |   .0192176   .0112849     1.70   0.089    -.0029005    .0413357
                 13  |   .0194031   .0108467     1.79   0.074    -.0018561    .0406624
                 14  |   .0195741   .0104231     1.88   0.060    -.0008549    .0400031
                 15  |   .0197305   .0100144     1.97   0.049     .0001027    .0393582
                 16  |   .0198722   .0096206     2.07   0.039     .0010162    .0387283
                 17  |   .0199994   .0092422     2.16   0.030     .0018851    .0381137
                 18  |   .0201119   .0088792     2.27   0.024      .002709    .0375149
                 19  |   .0202099    .008532     2.37   0.018     .0034874    .0369324
                 20  |   .0202933   .0082009     2.47   0.013     .0042199    .0363667
                 21  |    .020362   .0078859     2.58   0.010     .0049059    .0358181
                 22  |   .0204162   .0075874     2.69   0.007     .0055452    .0352872
                 23  |   .0204557   .0073055     2.80   0.005     .0061372    .0347743
                 24  |   .0204807   .0070404     2.91   0.004     .0066818    .0342796
                 25  |   .0204911   .0067921     3.02   0.003     .0071788    .0338034
                 26  |   .0204868   .0065608     3.12   0.002     .0076279    .0333457
                 27  |    .020468   .0063464     3.23   0.001     .0080293    .0329066
                 28  |   .0204345   .0061488     3.32   0.001     .0083832    .0324859
                 29  |   .0203865   .0059677     3.42   0.001     .0086899    .0320831
                 30  |   .0203239    .005803     3.50   0.000     .0089501    .0316976
                 31  |   .0202466   .0056542     3.58   0.000     .0091646    .0313287
                 32  |   .0201548   .0055207     3.65   0.000     .0093343    .0309752
                 33  |   .0200483    .005402     3.71   0.000     .0094606    .0306361
                 34  |   .0199273   .0052972     3.76   0.000      .009545    .0303096
                 35  |   .0197916   .0052055     3.80   0.000     .0095891    .0299942
                 36  |   .0196414   .0051259     3.83   0.000     .0095948     .029688
                 37  |   .0194766   .0050574     3.85   0.000     .0095643    .0293889
                 38  |   .0192971   .0049989     3.86   0.000     .0094995    .0290947
                 39  |   .0191031   .0049492     3.86   0.000     .0094029    .0288032
                 40  |   .0188944   .0049071     3.85   0.000     .0092767    .0285122
                 41  |   .0186712   .0048716     3.83   0.000     .0091231    .0282193
                 42  |   .0184333   .0048414     3.81   0.000     .0089444    .0279222
                 43  |   .0181809   .0048153     3.78   0.000      .008743    .0276188
                 44  |   .0179138   .0047925     3.74   0.000     .0085208    .0273069
                 45  |   .0176322   .0047716     3.70   0.000       .00828    .0269844
                 46  |   .0173359   .0047519     3.65   0.000     .0080224    .0266495
                 47  |   .0170251   .0047323     3.60   0.000       .00775    .0263002
                 48  |   .0166996    .004712     3.54   0.000     .0074643     .025935
                 49  |   .0163596   .0046902     3.49   0.000      .007167    .0255522
                 50  |   .0160049   .0046662     3.43   0.001     .0068595    .0251504
                 51  |   .0156357   .0046392     3.37   0.001      .006543    .0247284
                 52  |   .0152519   .0046087     3.31   0.001     .0062189    .0242848
                 53  |   .0148534   .0045742     3.25   0.001     .0058882    .0238186
                 54  |   .0144404    .004535     3.18   0.001     .0055519    .0233289
                 55  |   .0140127   .0044909     3.12   0.002     .0052108    .0228146
                 56  |   .0135705   .0044413     3.06   0.002     .0048657    .0222752
                 57  |   .0131136   .0043859     2.99   0.003     .0045173    .0217099
                 58  |   .0126422   .0043245     2.92   0.003     .0041663     .021118
                 59  |   .0121561   .0042567     2.86   0.004     .0038131    .0204991
                 60  |   .0116554   .0041824     2.79   0.005     .0034581    .0198528
                 61  |   .0111402   .0041013     2.72   0.007     .0031017    .0191786
                 62  |   .0106103   .0040134     2.64   0.008     .0027442    .0184765
                 63  |   .0100659   .0039186     2.57   0.010     .0023856    .0177462
                 64  |   .0095068   .0038168     2.49   0.013     .0020261    .0169876
                 65  |   .0089332   .0037081     2.41   0.016     .0016655    .0162008
                 66  |   .0083449   .0035925     2.32   0.020     .0013037    .0153861
                 67  |   .0077421   .0034703     2.23   0.026     .0009404    .0145438
                 68  |   .0071246   .0033418     2.13   0.033     .0005749    .0136743
                 69  |   .0064926   .0032072     2.02   0.043     .0002065    .0127786
                 70  |   .0058459   .0030673     1.91   0.057    -.0001658    .0118576
                 71  |   .0051847   .0029226     1.77   0.076    -.0005436    .0109129
                 72  |   .0045088   .0027744     1.63   0.104    -.0009289    .0099465
                 73  |   .0038183   .0026238     1.46   0.146    -.0013242    .0089609
                 74  |   .0031133   .0024728     1.26   0.208    -.0017333    .0079599
                 75  |   .0023936   .0023239     1.03   0.303     -.002161    .0069483
                 76  |   .0016594   .0021803     0.76   0.447    -.0026139    .0059326
                 77  |   .0009105   .0020465     0.44   0.656    -.0031005    .0049215
                 78  |   .0001471   .0019281     0.08   0.939     -.003632    .0039261
                 79  |   -.000631   .0018324    -0.34   0.731    -.0042224    .0029604
                 80  |  -.0014237   .0017673    -0.81   0.421    -.0048876    .0020402
                 81  |  -.0022309   .0017411    -1.28   0.200    -.0056434    .0011816
                 82  |  -.0030528   .0017603    -1.73   0.083     -.006503    .0003974
                 83  |  -.0038892   .0018285    -2.13   0.033     -.007473   -.0003055
                 84  |  -.0047403   .0019452    -2.44   0.015    -.0085528   -.0009278
                 85  |   -.005606    .002107    -2.66   0.008    -.0097355   -.0014764
                 86  |  -.0064862   .0023087    -2.81   0.005    -.0110112   -.0019612
                 87  |  -.0073811    .002545    -2.90   0.004    -.0123692    -.002393
                 88  |  -.0082905    .002811    -2.95   0.003       -.0138   -.0027811
                 89  |  -.0092146   .0031026    -2.97   0.003    -.0152956   -.0031335
                 90  |  -.0101533   .0034167    -2.97   0.003    -.0168499   -.0034566
                 91  |  -.0111065   .0037508    -2.96   0.003     -.018458    -.003755
                 92  |  -.0120744    .004103    -2.94   0.003    -.0201162   -.0040326
                 93  |  -.0130568   .0044719    -2.92   0.004    -.0218216   -.0042921
                 94  |  -.0140539   .0048563    -2.89   0.004     -.023572   -.0045358
                 95  |  -.0150656   .0052554    -2.87   0.004    -.0253659   -.0047653
                 96  |  -.0160918   .0056684    -2.84   0.005    -.0272018   -.0049819
                 97  |  -.0171327    .006095    -2.81   0.005    -.0290787   -.0051867
                 98  |  -.0181882   .0065346    -2.78   0.005    -.0309958   -.0053805
                 99  |  -.0192582    .006987    -2.76   0.006    -.0329524    -.005564
                100  |  -.0203429   .0074518    -2.73   0.006    -.0349481   -.0057377
                101  |  -.0214422   .0079288    -2.70   0.007    -.0369823    -.005902
--------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f China Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A7
. 
. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_UK_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_UK_lagged l4.support_US_
> std l4.support_USSR_std l4.support_China_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_UK, fe
>  cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_UK_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      7,003
Group variable: ccode                           Number of groups  =        191

R-squared:                                      Obs per group:
     Within  = 0.9599                                         min =          1
     Between = 0.9995                                         avg =       36.7
     Overall = 0.9930                                         max =         56

                                                F(16,190)         =    7574.77
corr(u_i, Xb) = 0.8490                          Prob > F          =     0.0000

                                                                       (Std. err. adjusted for 191 clusters in ccode)
---------------------------------------------------------------------------------------------------------------------
                                                    |               Robust
                                             Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------------------------------+----------------------------------------------------------------
                                             Nu_std |
                                                L1. |   .9332415   .0218203    42.77   0.000     .8902003    .9762827
                                                L2. |   .0222464   .0224936     0.99   0.324    -.0221228    .0666156
                                                    |
                                      Nu_std_lagged |  -.0517517   .0166463    -3.11   0.002    -.0845871   -.0189163
                                  support_UK_lagged |  -.0018433   .0218827    -0.08   0.933    -.0450076     .041321
                                                    |
                c.Nu_std_lagged#c.support_UK_lagged |   .0400004   .0860799     0.46   0.643    -.1297946    .2097953
                                                    |
                                      Nu_std_lagged |          0  (omitted)
                                                    |
                    c.Nu_std_lagged#c.Nu_std_lagged |   .0304207   .0109951     2.77   0.006     .0087326    .0521088
                                                    |
                                  support_UK_lagged |          0  (omitted)
                                                    |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_UK_lagged |  -.0655071   .0726013    -0.90   0.368    -.2087153    .0777011
                                                    |
                                     support_US_std |
                                                L4. |   .0004287   .0029523     0.15   0.885    -.0053948    .0062523
                                                    |
                                   support_USSR_std |
                                                L4. |   .0024042   .0034997     0.69   0.493     -.004499    .0093074
                                                    |
                                  support_China_std |
                                                L4. |   .0042506   .0031314     1.36   0.176    -.0019262    .0104274
                                                    |
                                 support_France_std |
                                                L4. |   .0037489    .001547     2.42   0.016     .0006975    .0068003
                                                    |
                                               cinc |
                                                L4. |   .3152131   .2652133     1.19   0.236    -.2079277    .8383539
                                                    |
                                        country_dem |
                                                L4. |   .0000966   .0007333     0.13   0.895      -.00135    .0015431
                                                    |
                                             ln_GNP |
                                                L4. |   .0021905   .0005277     4.15   0.000     .0011495    .0032314
                                                    |
                                             L4.mid |
                                                 1  |   -.000058   .0007381    -0.08   0.937    -.0015139     .001398
                                                    |
                              IdealPointDistance_UK |
                                                L4. |   .0001526   .0005512     0.28   0.782    -.0009347    .0012399
                                                    |
                                              _cons |  -.0085319   .0097536    -0.87   0.383    -.0277711    .0107073
----------------------------------------------------+----------------------------------------------------------------
                                            sigma_u |  .01519912
                                            sigma_e |  .01995765
                                                rho |  .36708319   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------------------------------

. estimates store p3

. margins, dydx(support_UK_lagged) at(Nu_std_lagged = (0(0.01)1)) force
note: default prediction is a function of possibly stochastic quantities other than e(b).

Average marginal effects                                 Number of obs = 7,003
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_UK_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
support_UK_lagged |
              _at |
               1  |  -.0018433   .0218827    -0.08   0.933    -.0447327    .0410461
               2  |  -.0014498   .0210557    -0.07   0.945    -.0427183    .0398186
               3  |  -.0010695   .0202444    -0.05   0.958    -.0407479    .0386089
               4  |  -.0007022    .019449    -0.04   0.971    -.0388215    .0374171
               5  |  -.0003481   .0186695    -0.02   0.985    -.0369396    .0362434
               6  |  -7.04e-06    .017906    -0.00   1.000    -.0351022    .0350881
               7  |   .0003209   .0171587     0.02   0.985    -.0333096    .0339514
               8  |   .0006357   .0164278     0.04   0.969    -.0315622    .0328337
               9  |   .0009375   .0157135     0.06   0.952    -.0298604    .0317353
              10  |   .0012261   .0150158     0.08   0.935    -.0282044    .0306566
              11  |   .0015017   .0143351     0.10   0.917    -.0265947     .029598
              12  |   .0017641   .0136716     0.13   0.897    -.0250318      .02856
              13  |   .0020134   .0130256     0.15   0.877    -.0235162    .0275431
              14  |   .0022497   .0123973     0.18   0.856    -.0220486     .026548
              15  |   .0024728   .0117872     0.21   0.834    -.0206297    .0255753
              16  |   .0026829   .0111956     0.24   0.811    -.0192601    .0246258
              17  |   .0028798    .010623     0.27   0.786    -.0179409    .0237005
              18  |   .0030636   .0100699     0.30   0.761     -.016673    .0228003
              19  |   .0032343   .0095369     0.34   0.735    -.0154576    .0219263
              20  |    .003392   .0090246     0.38   0.707     -.014296    .0210799
              21  |   .0035365   .0085338     0.41   0.679    -.0131895    .0202625
              22  |   .0036679   .0080654     0.45   0.649    -.0121399    .0194758
              23  |   .0037862   .0076202     0.50   0.619     -.011149    .0187215
              24  |   .0038915   .0071992     0.54   0.589    -.0102187    .0180017
              25  |   .0039836   .0068037     0.59   0.558    -.0093514    .0173186
              26  |   .0040626   .0064347     0.63   0.528    -.0085493    .0166745
              27  |   .0041285   .0060937     0.68   0.498    -.0078149    .0160719
              28  |   .0041813   .0057818     0.72   0.470    -.0071507    .0155134
              29  |    .004221   .0055002     0.77   0.443    -.0065592    .0150013
              30  |   .0042477   .0052502     0.81   0.418    -.0060426    .0145379
              31  |   .0042612   .0050325     0.85   0.397    -.0056024    .0141248
              32  |   .0042616   .0048478     0.88   0.379    -.0052399     .013763
              33  |   .0042489   .0046959     0.90   0.366     -.004955    .0134528
              34  |   .0042231   .0045766     0.92   0.356    -.0047468     .013193
              35  |   .0041842   .0044885     0.93   0.351    -.0046131    .0129815
              36  |   .0041322     .00443     0.93   0.351    -.0045504    .0128148
              37  |   .0040671   .0043987     0.92   0.355    -.0045542    .0126884
              38  |   .0039889   .0043919     0.91   0.364     -.004619    .0125968
              39  |   .0038976   .0044064     0.88   0.376    -.0047387     .012534
              40  |   .0037932    .004439     0.85   0.393    -.0049071    .0124935
              41  |   .0036757   .0044864     0.82   0.413    -.0051175     .012469
              42  |   .0035451   .0045455     0.78   0.435     -.005364    .0124542
              43  |   .0034014   .0046133     0.74   0.461    -.0056405    .0124433
              44  |   .0032446   .0046871     0.69   0.489    -.0059419    .0124311
              45  |   .0030747   .0047644     0.65   0.519    -.0062633    .0124127
              46  |   .0028917    .004843     0.60   0.550    -.0066004    .0123838
              47  |   .0026956   .0049211     0.55   0.584    -.0069495    .0123407
              48  |   .0024864   .0049969     0.50   0.619    -.0073074    .0122801
              49  |    .002264    .005069     0.45   0.655    -.0076711    .0121991
              50  |   .0020286   .0051362     0.39   0.693    -.0080382    .0120955
              51  |   .0017801   .0051974     0.34   0.732    -.0084067    .0119669
              52  |   .0015185   .0052517     0.29   0.772    -.0087747    .0118117
              53  |   .0012438   .0052983     0.23   0.814    -.0091407    .0116282
              54  |    .000956   .0053365     0.18   0.858    -.0095033    .0114152
              55  |    .000655   .0053657     0.12   0.903    -.0098615    .0111716
              56  |    .000341   .0053855     0.06   0.950    -.0102143    .0108963
              57  |   .0000139   .0053954     0.00   0.998    -.0105609    .0105887
              58  |  -.0003263   .0053951    -0.06   0.952    -.0109006    .0102479
              59  |  -.0006797   .0053844    -0.13   0.900    -.0112328    .0098735
              60  |  -.0010461   .0053629    -0.20   0.845    -.0115572     .009465
              61  |  -.0014256   .0053305    -0.27   0.789    -.0118733     .009022
              62  |  -.0018183   .0052872    -0.34   0.731     -.012181    .0085444
              63  |   -.002224   .0052327    -0.43   0.671      -.01248     .008032
              64  |  -.0026428   .0051672    -0.51   0.609    -.0127703    .0074846
              65  |  -.0030748   .0050905    -0.60   0.546     -.013052    .0069025
              66  |  -.0035198   .0050028    -0.70   0.482    -.0133252    .0062855
              67  |   -.003978   .0049042    -0.81   0.417    -.0135901    .0056342
              68  |  -.0044492    .004795    -0.93   0.353    -.0138471    .0049487
              69  |  -.0049335   .0046753    -1.06   0.291    -.0140969    .0042298
              70  |   -.005431   .0045455    -1.19   0.232    -.0143401    .0034781
              71  |  -.0059415   .0044063    -1.35   0.178    -.0145776    .0026946
              72  |  -.0064652   .0042581    -1.52   0.129    -.0148108    .0018805
              73  |  -.0070019   .0041018    -1.71   0.088    -.0150413    .0010375
              74  |  -.0075518   .0039385    -1.92   0.055    -.0152712    .0001676
              75  |  -.0081147   .0037696    -2.15   0.031     -.015503   -.0007264
              76  |  -.0086908   .0035968    -2.42   0.016    -.0157404   -.0016411
              77  |  -.0092799   .0034225    -2.71   0.007    -.0159879    -.002572
              78  |  -.0098822   .0032496    -3.04   0.002    -.0162512   -.0035132
              79  |  -.0104975   .0030819    -3.41   0.001    -.0165379   -.0044571
              80  |   -.011126   .0029244    -3.80   0.000    -.0168578   -.0053942
              81  |  -.0117676   .0027834    -4.23   0.000    -.0172229   -.0063122
              82  |  -.0124222   .0026663    -4.66   0.000     -.017648   -.0071964
              83  |    -.01309   .0025816    -5.07   0.000    -.0181498   -.0080301
              84  |  -.0137708   .0025383    -5.43   0.000    -.0187459   -.0087958
              85  |  -.0144648   .0025444    -5.68   0.000    -.0194517   -.0094779
              86  |  -.0151719   .0026055    -5.82   0.000    -.0202785   -.0100652
              87  |   -.015892   .0027238    -5.83   0.000    -.0212306   -.0105534
              88  |  -.0166253   .0028982    -5.74   0.000    -.0223057   -.0109449
              89  |  -.0173717   .0031248    -5.56   0.000    -.0234963   -.0112471
              90  |  -.0181312   .0033986    -5.33   0.000    -.0247923     -.01147
              91  |  -.0189037   .0037141    -5.09   0.000    -.0261833   -.0116241
              92  |  -.0196894   .0040665    -4.84   0.000    -.0276595   -.0117193
              93  |  -.0204882   .0044513    -4.60   0.000    -.0292127   -.0117637
              94  |  -.0213001   .0048653    -4.38   0.000    -.0308359   -.0117642
              95  |   -.022125   .0053055    -4.17   0.000    -.0325237   -.0117264
              96  |  -.0229631   .0057698    -3.98   0.000    -.0342717   -.0116545
              97  |  -.0238143   .0062564    -3.81   0.000    -.0360766    -.011552
              98  |  -.0246786   .0067639    -3.65   0.000    -.0379356   -.0114216
              99  |   -.025556   .0072912    -3.51   0.000    -.0398464   -.0112655
             100  |  -.0264465   .0078374    -3.37   0.001    -.0418075   -.0110854
             101  |    -.02735   .0084019    -3.26   0.001    -.0438174   -.0108827
-----------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f UK Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A8
. 
. 
. xtreg Nu_std l.Nu_std l2.Nu_std c.Nu_std_lagged##c.support_France_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_France_lagged l4.sup
> port_US_std l4.support_USSR_std l4.support_China_std l4.support_UK_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_Fr
> ance, fe cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_France_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      6,997
Group variable: ccode                           Number of groups  =        190

R-squared:                                      Obs per group:
     Within  = 0.9600                                         min =          1
     Between = 0.9995                                         avg =       36.8
     Overall = 0.9930                                         max =         56

                                                F(16,189)         =    7182.07
corr(u_i, Xb) = 0.8411                          Prob > F          =     0.0000

                                                                           (Std. err. adjusted for 190 clusters in ccode)
-------------------------------------------------------------------------------------------------------------------------
                                                        |               Robust
                                                 Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------------------------+----------------------------------------------------------------
                                                 Nu_std |
                                                    L1. |   .9318735   .0220024    42.35   0.000     .8884717    .9752753
                                                    L2. |    .022438   .0224875     1.00   0.320    -.0219208    .0667968
                                                        |
                                          Nu_std_lagged |  -.0523215   .0170473    -3.07   0.002     -.085949    -.018694
                                  support_France_lagged |   .0053201   .0129541     0.41   0.682    -.0202332    .0308733
                                                        |
                c.Nu_std_lagged#c.support_France_lagged |    .023165   .0530668     0.44   0.663    -.0815143    .1278443
                                                        |
                                          Nu_std_lagged |          0  (omitted)
                                                        |
                        c.Nu_std_lagged#c.Nu_std_lagged |   .0304859   .0110286     2.76   0.006     .0087308    .0522409
                                                        |
                                  support_France_lagged |          0  (omitted)
                                                        |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_France_lagged |  -.0457803   .0458413    -1.00   0.319    -.1362066    .0446459
                                                        |
                                         support_US_std |
                                                    L4. |   .0017451   .0029515     0.59   0.555    -.0040769    .0075672
                                                        |
                                       support_USSR_std |
                                                    L4. |   .0039563   .0033596     1.18   0.240    -.0026708    .0105834
                                                        |
                                      support_China_std |
                                                    L4. |   .0051363   .0030752     1.67   0.097    -.0009298    .0112024
                                                        |
                                         support_UK_std |
                                                    L4. |   -.007575   .0023775    -3.19   0.002    -.0122648   -.0028853
                                                        |
                                                   cinc |
                                                    L4. |   .3293063   .2897752     1.14   0.257    -.2423029    .9009155
                                                        |
                                            country_dem |
                                                    L4. |    .000238   .0007382     0.32   0.748    -.0012182    .0016942
                                                        |
                                                 ln_GNP |
                                                    L4. |   .0024502   .0005519     4.44   0.000     .0013616    .0035388
                                                        |
                                                 L4.mid |
                                                     1  |  -.0000485   .0007376    -0.07   0.948    -.0015035    .0014065
                                                        |
                              IdealPointDistance_France |
                                                    L4. |   -.000736   .0006051    -1.22   0.225    -.0019296    .0004576
                                                        |
                                                  _cons |  -.0117224   .0098739    -1.19   0.237    -.0311997    .0077548
--------------------------------------------------------+----------------------------------------------------------------
                                                sigma_u |  .01508561
                                                sigma_e |  .01994189
                                                    rho |  .36397277   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------------------------------------

. estimates store p4

. margins, dydx(support_France_lagged) at(Nu_std_lagged = (0(0.01)1)) force
note: default prediction is a function of possibly stochastic quantities other than e(b).

Average marginal effects                                 Number of obs = 6,997
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_France_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
support_France_lagged |
                  _at |
                   1  |   .0053201   .0129541     0.41   0.681    -.0200695    .0307097
                   2  |   .0055471   .0124405     0.45   0.656    -.0188357      .02993
                   3  |   .0057651   .0119366     0.48   0.629    -.0176302    .0291603
                   4  |   .0059738   .0114425     0.52   0.602    -.0164531    .0284008
                   5  |   .0061734   .0109584     0.56   0.573    -.0153046    .0276514
                   6  |   .0063639   .0104842     0.61   0.544    -.0141847    .0269124
                   7  |   .0065452     .01002     0.65   0.514    -.0130937     .026184
                   8  |   .0067173   .0095659     0.70   0.483    -.0120316    .0254662
                   9  |   .0068803   .0091221     0.75   0.451    -.0109988    .0247593
                  10  |   .0070341   .0086886     0.81   0.418    -.0099953    .0240635
                  11  |   .0071788   .0082656     0.87   0.385    -.0090216    .0233791
                  12  |   .0073143   .0078532     0.93   0.352    -.0080778    .0227063
                  13  |   .0074406   .0074516     1.00   0.318    -.0071643    .0220455
                  14  |   .0075578    .007061     1.07   0.284    -.0062814    .0213971
                  15  |   .0076659   .0066815     1.15   0.251    -.0054297    .0207615
                  16  |   .0077648   .0063136     1.23   0.219    -.0046096    .0201391
                  17  |   .0078545   .0059574     1.32   0.187    -.0038217    .0195307
                  18  |   .0079351   .0056133     1.41   0.157    -.0030667    .0189369
                  19  |   .0080065   .0052817     1.52   0.130    -.0023454    .0183584
                  20  |   .0080688   .0049631     1.63   0.104    -.0016587    .0177962
                  21  |   .0081219    .004658     1.74   0.081    -.0010077    .0172514
                  22  |   .0081658   .0043672     1.87   0.062    -.0003937    .0167253
                  23  |   .0082006   .0040912     2.00   0.045      .000182    .0162192
                  24  |   .0082262    .003831     2.15   0.032     .0007176    .0157349
                  25  |   .0082427   .0035875     2.30   0.022     .0012113    .0152742
                  26  |   .0082501   .0033619     2.45   0.014     .0016609    .0148392
                  27  |   .0082482   .0031552     2.61   0.009     .0020642    .0144323
                  28  |   .0082372   .0029687     2.77   0.006     .0024186    .0140559
                  29  |   .0082171   .0028038     2.93   0.003     .0027218    .0137124
                  30  |   .0081878   .0026614     3.08   0.002     .0029716     .013404
                  31  |   .0081493   .0025425     3.21   0.001     .0031661    .0131326
                  32  |   .0081017   .0024477     3.31   0.001     .0033043    .0128991
                  33  |    .008045   .0023769     3.38   0.001     .0033862    .0127037
                  34  |    .007979   .0023296     3.43   0.001     .0034131     .012545
                  35  |    .007904   .0023044     3.43   0.001     .0033874    .0124206
                  36  |   .0078197   .0022994     3.40   0.001     .0033129    .0123266
                  37  |   .0077263   .0023123     3.34   0.001     .0031943    .0122583
                  38  |   .0076238   .0023403     3.26   0.001     .0030369    .0122107
                  39  |   .0075121   .0023807     3.16   0.002      .002846    .0121782
                  40  |   .0073912   .0024308     3.04   0.002      .002627    .0121555
                  41  |   .0072612    .002488     2.92   0.004     .0023848    .0121377
                  42  |   .0071221   .0025502     2.79   0.005     .0021237    .0121204
                  43  |   .0069737   .0026153     2.67   0.008     .0018479    .0120996
                  44  |   .0068162   .0026815     2.54   0.011     .0015605     .012072
                  45  |   .0066496   .0027475     2.42   0.016     .0012646    .0120346
                  46  |   .0064738    .002812     2.30   0.021     .0009624    .0119852
                  47  |   .0062889   .0028739     2.19   0.029     .0006562    .0119215
                  48  |   .0060948   .0029323     2.08   0.038     .0003476    .0118419
                  49  |   .0058915   .0029865     1.97   0.049      .000038     .011745
                  50  |   .0056791    .003036     1.87   0.061    -.0002714    .0116295
                  51  |   .0054575   .0030801     1.77   0.076    -.0005795    .0114945
                  52  |   .0052268   .0031186     1.68   0.094    -.0008855     .011339
                  53  |   .0049869   .0031509     1.58   0.113    -.0011887    .0111625
                  54  |   .0047378   .0031768     1.49   0.136    -.0014886    .0109643
                  55  |   .0044796   .0031961     1.40   0.161    -.0017847    .0107439
                  56  |   .0042123   .0032086     1.31   0.189    -.0020765     .010501
                  57  |   .0039358   .0032141     1.22   0.221    -.0023637    .0102352
                  58  |   .0036501   .0032124     1.14   0.256    -.0026461    .0099463
                  59  |   .0033553   .0032036     1.05   0.295    -.0029236    .0096341
                  60  |   .0030513   .0031874     0.96   0.338    -.0031959    .0092985
                  61  |   .0027382   .0031639     0.87   0.387     -.003463    .0089394
                  62  |   .0024159   .0031331     0.77   0.441    -.0037249    .0085567
                  63  |   .0020844    .003095     0.67   0.501    -.0039817    .0081505
                  64  |   .0017438   .0030496     0.57   0.567    -.0042333    .0077209
                  65  |   .0013941    .002997     0.47   0.642      -.00448    .0072681
                  66  |   .0010351   .0029374     0.35   0.725    -.0047221    .0067923
                  67  |   .0006671   .0028709     0.23   0.816    -.0049597    .0062939
                  68  |   .0002898   .0027977     0.10   0.917    -.0051935    .0057732
                  69  |  -.0000965   .0027181    -0.04   0.972    -.0054239    .0052309
                  70  |  -.0004921   .0026325    -0.19   0.852    -.0056517    .0046676
                  71  |  -.0008968   .0025414    -0.35   0.724    -.0058779    .0040843
                  72  |  -.0013106   .0024454    -0.54   0.592    -.0061035    .0034822
                  73  |  -.0017336   .0023452    -0.74   0.460    -.0063302    .0028629
                  74  |  -.0021658   .0022419    -0.97   0.334    -.0065599    .0022283
                  75  |  -.0026071   .0021368    -1.22   0.222    -.0067952    .0015809
                  76  |  -.0030576   .0020316    -1.51   0.132    -.0070394    .0009242
                  77  |  -.0035172   .0019284    -1.82   0.068    -.0072969    .0002624
                  78  |   -.003986   .0018301    -2.18   0.029    -.0075731    -.000399
                  79  |   -.004464   .0017404    -2.56   0.010     -.007875   -.0010529
                  80  |  -.0049511   .0016634    -2.98   0.003    -.0082113   -.0016908
                  81  |  -.0054473   .0016045    -3.40   0.001    -.0085921   -.0023026
                  82  |  -.0059527   .0015691    -3.79   0.000    -.0090282   -.0028773
                  83  |  -.0064673   .0015627    -4.14   0.000    -.0095301   -.0034045
                  84  |   -.006991   .0015892    -4.40   0.000    -.0101059   -.0038762
                  85  |  -.0075239   .0016511    -4.56   0.000    -.0107599   -.0042879
                  86  |   -.008066   .0017481    -4.61   0.000    -.0114922   -.0046397
                  87  |  -.0086172   .0018786    -4.59   0.000    -.0122991   -.0049352
                  88  |  -.0091775   .0020395    -4.50   0.000    -.0131748   -.0051802
                  89  |   -.009747   .0022275    -4.38   0.000    -.0141129   -.0053812
                  90  |  -.0103257   .0024395    -4.23   0.000     -.015107   -.0055444
                  91  |  -.0109135   .0026726    -4.08   0.000    -.0161517   -.0056752
                  92  |  -.0115105   .0029246    -3.94   0.000    -.0172426   -.0057783
                  93  |  -.0121166   .0031936    -3.79   0.000     -.018376   -.0058572
                  94  |  -.0127319   .0034781    -3.66   0.000    -.0195489   -.0059149
                  95  |  -.0133563    .003777    -3.54   0.000     -.020759   -.0059536
                  96  |  -.0139899   .0040892    -3.42   0.001    -.0220046   -.0059752
                  97  |  -.0146327   .0044141    -3.31   0.001    -.0232842   -.0059811
                  98  |  -.0152846   .0047511    -3.22   0.001    -.0245966   -.0059725
                  99  |  -.0159456   .0050998    -3.13   0.002     -.025941   -.0059503
                 100  |  -.0166159   .0054596    -3.04   0.002    -.0273165   -.0059152
                 101  |  -.0172952   .0058304    -2.97   0.003    -.0287226   -.0058679
---------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f French Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A9
. 
. esttab p1 p2 p3 p4 using TableA5.tex, se star(+ 0.10 * 0.05 ** 0.01) replace
(file TableA5.tex not found)
(output written to TableA5.tex)

. 
. 
. 
. ***************************************************************************************************
. * Table A6
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. 
. sort ccode year

. xi i.year
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

. 
. xtset ccode year

Panel variable: ccode (unbalanced)
 Time variable: year, 1950 to 2010, but with a gap
         Delta: 1 unit

. 
. gen Nu_std_lagged = l.Nu_std
(200 missing values generated)

. 
. gen support_USSR_lagged = l.support_USSR_std
(199 missing values generated)

. 
. 
. xtreg Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged if year <= 1991, fe cluster(c
> code)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      5,046
Group variable: ccode                           Number of groups  =        164

R-squared:                                      Obs per group:
     Within  = 0.9477                                         min =          1
     Between = 0.9981                                         avg =       30.8
     Overall = 0.9883                                         max =         41

                                                F(5,163)          =   16266.16
corr(u_i, Xb) = 0.7549                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 164 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                        Nu_std_lagged |   .8960969   .0118443    75.66   0.000     .8727088     .919485
                                  support_USSR_lagged |  -.0183476   .0202002    -0.91   0.365    -.0582354    .0215403
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |   .1673562     .07484     2.24   0.027     .0195753     .315137
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .0602152    .010342     5.82   0.000     .0397936    .0806369
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.1469226   .0603983    -2.43   0.016    -.2661865   -.0276587
                                                      |
                                                _cons |   .0354875   .0028207    12.58   0.000     .0299178    .0410573
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |  .01542382
                                              sigma_e |  .02498115
                                                  rho |  .27599471   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store USSRp1

. 
. xtreg Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged l2.support_US_std l2.support_
> China_std l2.support_UK_std l2.support_France_std l2.cinc l2.country_dem l2.ln_GNP l2.i.mid l2.IdealPointDistance_USSR if year <= 1991, f
> e cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      4,241
Group variable: ccode                           Number of groups  =        153

R-squared:                                      Obs per group:
     Within  = 0.9447                                         min =          6
     Between = 0.9977                                         avg =       27.7
     Overall = 0.9874                                         max =         39

                                                F(14,152)         =    3801.59
corr(u_i, Xb) = 0.7110                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 153 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                        Nu_std_lagged |    .844607   .0171773    49.17   0.000     .8106699    .8785441
                                  support_USSR_lagged |  -.0468262   .0275829    -1.70   0.092    -.1013215    .0076691
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |   .1731832   .1088805     1.59   0.114    -.0419313    .3882977
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .0504614   .0153687     3.28   0.001     .0200975    .0808252
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.1354633   .0907033    -1.49   0.137    -.3146652    .0437387
                                                      |
                                       support_US_std |
                                                  L2. |   .0010892   .0047914     0.23   0.820    -.0083771    .0105556
                                                      |
                                    support_China_std |
                                                  L2. |   .0076687   .0035498     2.16   0.032     .0006554    .0146821
                                                      |
                                       support_UK_std |
                                                  L2. |  -.0257832   .0049998    -5.16   0.000    -.0356613   -.0159051
                                                      |
                                   support_France_std |
                                                  L2. |   .0083233   .0040183     2.07   0.040     .0003845    .0162622
                                                      |
                                                 cinc |
                                                  L2. |   .7660126   .5940474     1.29   0.199    -.4076432    1.939668
                                                      |
                                          country_dem |
                                                  L2. |  -.0044792   .0010253    -4.37   0.000    -.0065049   -.0024535
                                                      |
                                               ln_GNP |
                                                  L2. |   .0056753   .0008856     6.41   0.000     .0039257    .0074249
                                                      |
                                               L2.mid |
                                                   1  |   .0001343   .0009361     0.14   0.886    -.0017152    .0019838
                                                      |
                              IdealPointDistance_USSR |
                                                  L2. |  -.0019257   .0005944    -3.24   0.001    -.0030999   -.0007514
                                                      |
                                                _cons |  -.0576757   .0160807    -3.59   0.000    -.0894463    -.025905
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |  .01943371
                                              sigma_e |  .02387862
                                                  rho |  .39844483   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store USSRp2

. margins, dydx(support_USSR_lagged) at(Nu_std_lagged = (0(0.01)1)) force

Average marginal effects                                 Number of obs = 4,241
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_USSR_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
support_USSR_lagged |
                _at |
                 1  |  -.0468262   .0275829    -1.70   0.090    -.1008876    .0072353
                 2  |  -.0451079   .0265338    -1.70   0.089    -.0971131    .0068973
                 3  |  -.0434167   .0255041    -1.70   0.089    -.0934038    .0065704
                 4  |  -.0417526   .0244939    -1.70   0.088    -.0897599    .0062546
                 5  |  -.0401156   .0235035    -1.71   0.088    -.0861816    .0059504
                 6  |  -.0385057   .0225329    -1.71   0.087    -.0826693     .005658
                 7  |  -.0369229   .0215823    -1.71   0.087    -.0792235    .0053777
                 8  |  -.0353671    .020652    -1.71   0.087    -.0758443      .00511
                 9  |  -.0338385   .0197421    -1.71   0.087    -.0725324    .0048554
                10  |   -.032337    .018853    -1.72   0.086    -.0692882    .0046143
                11  |  -.0308625   .0179849    -1.72   0.086    -.0661123    .0043873
                12  |  -.0294151   .0171382    -1.72   0.086    -.0630053    .0041751
                13  |  -.0279949   .0163132    -1.72   0.086    -.0599681    .0039784
                14  |  -.0266017   .0155104    -1.72   0.086    -.0570015    .0037981
                15  |  -.0252356   .0147303    -1.71   0.087    -.0541064    .0036352
                16  |  -.0238966   .0139734    -1.71   0.087    -.0512841    .0034908
                17  |  -.0225847   .0132406    -1.71   0.088    -.0485358    .0033663
                18  |  -.0212999   .0125324    -1.70   0.089    -.0458629    .0032631
                19  |  -.0200422   .0118498    -1.69   0.091    -.0432674    .0031829
                20  |  -.0188116   .0111938    -1.68   0.093     -.040751    .0031278
                21  |  -.0176081   .0105655    -1.67   0.096    -.0383161       .0031
                22  |  -.0164317   .0099664    -1.65   0.099    -.0359654    .0031021
                23  |  -.0152823   .0093978    -1.63   0.104    -.0337017     .003137
                24  |  -.0141601   .0088615    -1.60   0.110    -.0315283    .0032082
                25  |  -.0130649   .0083594    -1.56   0.118    -.0294491    .0033193
                26  |  -.0119969   .0078936    -1.52   0.129     -.027468    .0034743
                27  |  -.0109559   .0074662    -1.47   0.142    -.0255894    .0036776
                28  |   -.009942   .0070796    -1.40   0.160    -.0238177    .0039337
                29  |  -.0089552   .0067359    -1.33   0.184    -.0221574     .004247
                30  |  -.0079955   .0064373    -1.24   0.214    -.0206124    .0046214
                31  |  -.0070629   .0061854    -1.14   0.254     -.019186    .0050601
                32  |  -.0061574   .0059811    -1.03   0.303    -.0178801    .0055652
                33  |   -.005279   .0058246    -0.91   0.365    -.0166951     .006137
                34  |  -.0044277   .0057152    -0.77   0.439    -.0156292    .0067738
                35  |  -.0036035   .0056508    -0.64   0.524    -.0146788    .0074719
                36  |  -.0028063   .0056286    -0.50   0.618    -.0138381    .0082255
                37  |  -.0020363   .0056447    -0.36   0.718    -.0130997    .0090271
                38  |  -.0012933   .0056947    -0.23   0.820    -.0124548    .0098681
                39  |  -.0005775   .0057739    -0.10   0.920    -.0118942    .0107392
                40  |   .0001113   .0058775     0.02   0.985    -.0114085     .011631
                41  |    .000773   .0060008     0.13   0.898    -.0109885    .0125344
                42  |   .0014075   .0061395     0.23   0.819    -.0106257    .0134408
                43  |    .002015   .0062897     0.32   0.749    -.0103125    .0143425
                44  |   .0025954   .0064478     0.40   0.687     -.010042    .0152328
                45  |   .0031487   .0066108     0.48   0.634    -.0098081    .0161056
                46  |   .0036749    .006776     0.54   0.588    -.0096059    .0169557
                47  |    .004174   .0069413     0.60   0.548    -.0094308    .0177788
                48  |   .0046461   .0071048     0.65   0.513    -.0092791    .0185712
                49  |    .005091   .0072648     0.70   0.483    -.0091478    .0193298
                50  |   .0055088   .0074201     0.74   0.458    -.0090343     .020052
                51  |   .0058996   .0075695     0.78   0.436    -.0089364    .0207355
                52  |   .0062632   .0077122     0.81   0.417    -.0088524    .0213788
                53  |   .0065998   .0078474     0.84   0.400    -.0087808    .0219804
                54  |   .0069093   .0079746     0.87   0.386    -.0087207    .0225392
                55  |   .0071916   .0080934     0.89   0.374    -.0086711    .0230543
                56  |   .0074469   .0082034     0.91   0.364    -.0086314    .0235252
                57  |   .0076751   .0083044     0.92   0.355    -.0086013    .0239515
                58  |   .0078762   .0083965     0.94   0.348    -.0085806     .024333
                59  |   .0080502   .0084794     0.95   0.342    -.0085691    .0246695
                60  |   .0081971   .0085534     0.96   0.338    -.0085672    .0249614
                61  |   .0083169   .0086185     0.97   0.335    -.0085751     .025209
                62  |   .0084097   .0086751     0.97   0.332    -.0085933    .0254126
                63  |   .0084753   .0087235     0.97   0.331    -.0086224     .025573
                64  |   .0085138    .008764     0.97   0.331    -.0086632    .0256909
                65  |   .0085253   .0087971     0.97   0.332    -.0087168    .0257673
                66  |   .0085096   .0088235     0.96   0.335    -.0087841    .0258034
                67  |   .0084669   .0088438     0.96   0.338    -.0088665    .0258003
                68  |   .0083971   .0088587     0.95   0.343    -.0089656    .0257597
                69  |   .0083001   .0088691     0.94   0.349    -.0090829    .0256832
                70  |   .0081761   .0088759     0.92   0.357    -.0092203    .0255726
                71  |    .008025   .0088802     0.90   0.366    -.0093799      .02543
                72  |   .0078468   .0088832     0.88   0.377     -.009564    .0252576
                73  |   .0076415   .0088862     0.86   0.390     -.009775    .0250581
                74  |   .0074091   .0088904     0.83   0.405    -.0100157     .024834
                75  |   .0071497   .0088974     0.80   0.422    -.0102889    .0245882
                76  |   .0068631   .0089088     0.77   0.441    -.0105978     .024324
                77  |   .0065494   .0089262     0.73   0.463    -.0109456    .0240445
                78  |   .0062087   .0089515     0.69   0.488    -.0113359    .0237533
                79  |   .0058408   .0089865     0.65   0.516    -.0117723     .023454
                80  |   .0054459    .009033     0.60   0.547    -.0122585    .0231502
                81  |   .0050239   .0090931     0.55   0.581    -.0127982    .0228459
                82  |   .0045747   .0091685     0.50   0.618    -.0133953    .0225447
                83  |   .0040985   .0092614     0.44   0.658    -.0140535    .0222505
                84  |   .0035952   .0093734     0.38   0.701    -.0147763    .0219667
                85  |   .0030648   .0095064     0.32   0.747    -.0155673    .0216969
                86  |   .0025073   .0096619     0.26   0.795    -.0164297    .0214443
                87  |   .0019227   .0098415     0.20   0.845    -.0173662    .0212116
                88  |    .001311   .0100464     0.13   0.896    -.0183796    .0210016
                89  |   .0006722   .0102778     0.07   0.948    -.0194719    .0208164
                90  |   6.38e-06   .0105366     0.00   1.000    -.0206449    .0206577
                91  |  -.0006866   .0108235    -0.06   0.949    -.0219002     .020527
                92  |  -.0014066    .011139    -0.13   0.900    -.0232386    .0204253
                93  |  -.0021538   .0114834    -0.19   0.851    -.0246609    .0203533
                94  |   -.002928    .011857    -0.25   0.805    -.0261674    .0203114
                95  |  -.0037294   .0122598    -0.30   0.761    -.0277581    .0202994
                96  |  -.0045578   .0126916    -0.36   0.720    -.0294329    .0203174
                97  |  -.0054133   .0131523    -0.41   0.681    -.0311914    .0203648
                98  |  -.0062959   .0136416    -0.46   0.644     -.033033    .0204411
                99  |  -.0072056   .0141591    -0.51   0.611    -.0349569    .0205457
               100  |  -.0081424   .0147044    -0.55   0.580    -.0369624    .0206776
               101  |  -.0091063    .015277    -0.60   0.551    -.0390487    .0208362
-------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f USSR Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A10
. 
. 
. esttab USSRp1 USSRp2 using TableA6.tex, se star(+ 0.10 * 0.05 ** 0.01) replace
(file TableA6.tex not found)
(output written to TableA6.tex)

. 
. 
. 
. 
. ***************************************************************************************************
. * Table A7
. 
. use "nuclear_latency_panel.dta", clear
(Written by R.              )

. 
. sort ccode year

. xi i.year
i.year            _Iyear_1950-2010    (naturally coded; _Iyear_1950 omitted)

. 
. xtset ccode year

Panel variable: ccode (unbalanced)
 Time variable: year, 1950 to 2010, but with a gap
         Delta: 1 unit

. 
. gen Nu_std_lagged = l3.Nu_std
(595 missing values generated)

. 
. gen support_USSR_lagged = l3.support_USSR_std
(594 missing values generated)

. 
. 
. xtreg Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged if year <= 1991, fe cluster(c
> code)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      4,723
Group variable: ccode                           Number of groups  =        158

R-squared:                                      Obs per group:
     Within  = 0.8453                                         min =          5
     Between = 0.9913                                         avg =       29.9
     Overall = 0.9606                                         max =         39

                                                F(5,157)          =    1594.52
corr(u_i, Xb) = 0.7509                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 158 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                        Nu_std_lagged |     .71356    .030414    23.46   0.000     .6534866    .7736334
                                  support_USSR_lagged |   .0379946   .0571733     0.66   0.507    -.0749336    .1509228
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |   .1943604   .2160733     0.90   0.370    -.2324253     .621146
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .1546632   .0277274     5.58   0.000     .0998963      .20943
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.2051399   .1767364    -1.16   0.248    -.5542278    .1439479
                                                      |
                                                _cons |   .1007365   .0070399    14.31   0.000     .0868315    .1146416
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |  .04396064
                                              sigma_e |  .04049629
                                                  rho |  .54095023   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store USSRp1

. 
. xtreg Nu_std c.Nu_std_lagged##c.support_USSR_lagged c.Nu_std_lagged##c.Nu_std_lagged##c.support_USSR_lagged l4.support_US_std l4.support_
> China_std l4.support_UK_std l4.support_France_std l4.cinc l4.country_dem l4.ln_GNP l4.i.mid l4.IdealPointDistance_USSR if year <= 1991, f
> e cluster(ccode)
note: Nu_std_lagged omitted because of collinearity.
note: support_USSR_lagged omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      3,942
Group variable: ccode                           Number of groups  =        153

R-squared:                                      Obs per group:
     Within  = 0.8467                                         min =          4
     Between = 0.9768                                         avg =       25.8
     Overall = 0.9480                                         max =         37

                                                F(14,152)         =     396.63
corr(u_i, Xb) = 0.6786                          Prob > F          =     0.0000

                                                                         (Std. err. adjusted for 153 clusters in ccode)
-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               Nu_std | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                                        Nu_std_lagged |    .578122   .0387676    14.91   0.000     .5015291    .6547149
                                  support_USSR_lagged |  -.0052457   .0804015    -0.07   0.948    -.1640944     .153603
                                                      |
                c.Nu_std_lagged#c.support_USSR_lagged |   .0410257   .3216434     0.13   0.899    -.5944432    .6764945
                                                      |
                                        Nu_std_lagged |          0  (omitted)
                                                      |
                      c.Nu_std_lagged#c.Nu_std_lagged |   .1078662   .0396139     2.72   0.007     .0296013    .1861311
                                                      |
                                  support_USSR_lagged |          0  (omitted)
                                                      |
c.Nu_std_lagged#c.Nu_std_lagged#c.support_USSR_lagged |  -.0229097   .2681316    -0.09   0.932    -.5526557    .5068363
                                                      |
                                       support_US_std |
                                                  L4. |   .0132646   .0105146     1.26   0.209     -.007509    .0340382
                                                      |
                                    support_China_std |
                                                  L4. |    .040229   .0102794     3.91   0.000     .0199201     .060538
                                                      |
                                       support_UK_std |
                                                  L4. |  -.0774327   .0110842    -6.99   0.000    -.0993317   -.0555337
                                                      |
                                   support_France_std |
                                                  L4. |   .0326662   .0102644     3.18   0.002     .0123869    .0529455
                                                      |
                                                 cinc |
                                                  L4. |   2.490316   1.804529     1.38   0.170     -1.07488    6.055512
                                                      |
                                          country_dem |
                                                  L4. |  -.0099216   .0026469    -3.75   0.000     -.015151   -.0046922
                                                      |
                                               ln_GNP |
                                                  L4. |   .0146793   .0023549     6.23   0.000     .0100268    .0193318
                                                      |
                                               L4.mid |
                                                   1  |   .0002756   .0024293     0.11   0.910     -.004524    .0050752
                                                      |
                              IdealPointDistance_USSR |
                                                  L4. |  -.0056495   .0015651    -3.61   0.000    -.0087417   -.0025574
                                                      |
                                                _cons |  -.1380929   .0462457    -2.99   0.003    -.2294602   -.0467255
------------------------------------------------------+----------------------------------------------------------------
                                              sigma_u |  .05858517
                                              sigma_e |   .0370678
                                                  rho |  .71411733   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------------------------------------

. estimates store USSRp2

. margins, dydx(support_USSR_lagged) at(Nu_std_lagged = (0(0.01)1)) force

Average marginal effects                                 Number of obs = 3,942
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  support_USSR_lagged
1._at:   Nu_std_lagged =   0
2._at:   Nu_std_lagged = .01
3._at:   Nu_std_lagged = .02
4._at:   Nu_std_lagged = .03
5._at:   Nu_std_lagged = .04
6._at:   Nu_std_lagged = .05
7._at:   Nu_std_lagged = .06
8._at:   Nu_std_lagged = .07
9._at:   Nu_std_lagged = .08
10._at:  Nu_std_lagged = .09
11._at:  Nu_std_lagged =  .1
12._at:  Nu_std_lagged = .11
13._at:  Nu_std_lagged = .12
14._at:  Nu_std_lagged = .13
15._at:  Nu_std_lagged = .14
16._at:  Nu_std_lagged = .15
17._at:  Nu_std_lagged = .16
18._at:  Nu_std_lagged = .17
19._at:  Nu_std_lagged = .18
20._at:  Nu_std_lagged = .19
21._at:  Nu_std_lagged =  .2
22._at:  Nu_std_lagged = .21
23._at:  Nu_std_lagged = .22
24._at:  Nu_std_lagged = .23
25._at:  Nu_std_lagged = .24
26._at:  Nu_std_lagged = .25
27._at:  Nu_std_lagged = .26
28._at:  Nu_std_lagged = .27
29._at:  Nu_std_lagged = .28
30._at:  Nu_std_lagged = .29
31._at:  Nu_std_lagged =  .3
32._at:  Nu_std_lagged = .31
33._at:  Nu_std_lagged = .32
34._at:  Nu_std_lagged = .33
35._at:  Nu_std_lagged = .34
36._at:  Nu_std_lagged = .35
37._at:  Nu_std_lagged = .36
38._at:  Nu_std_lagged = .37
39._at:  Nu_std_lagged = .38
40._at:  Nu_std_lagged = .39
41._at:  Nu_std_lagged =  .4
42._at:  Nu_std_lagged = .41
43._at:  Nu_std_lagged = .42
44._at:  Nu_std_lagged = .43
45._at:  Nu_std_lagged = .44
46._at:  Nu_std_lagged = .45
47._at:  Nu_std_lagged = .46
48._at:  Nu_std_lagged = .47
49._at:  Nu_std_lagged = .48
50._at:  Nu_std_lagged = .49
51._at:  Nu_std_lagged =  .5
52._at:  Nu_std_lagged = .51
53._at:  Nu_std_lagged = .52
54._at:  Nu_std_lagged = .53
55._at:  Nu_std_lagged = .54
56._at:  Nu_std_lagged = .55
57._at:  Nu_std_lagged = .56
58._at:  Nu_std_lagged = .57
59._at:  Nu_std_lagged = .58
60._at:  Nu_std_lagged = .59
61._at:  Nu_std_lagged =  .6
62._at:  Nu_std_lagged = .61
63._at:  Nu_std_lagged = .62
64._at:  Nu_std_lagged = .63
65._at:  Nu_std_lagged = .64
66._at:  Nu_std_lagged = .65
67._at:  Nu_std_lagged = .66
68._at:  Nu_std_lagged = .67
69._at:  Nu_std_lagged = .68
70._at:  Nu_std_lagged = .69
71._at:  Nu_std_lagged =  .7
72._at:  Nu_std_lagged = .71
73._at:  Nu_std_lagged = .72
74._at:  Nu_std_lagged = .73
75._at:  Nu_std_lagged = .74
76._at:  Nu_std_lagged = .75
77._at:  Nu_std_lagged = .76
78._at:  Nu_std_lagged = .77
79._at:  Nu_std_lagged = .78
80._at:  Nu_std_lagged = .79
81._at:  Nu_std_lagged =  .8
82._at:  Nu_std_lagged = .81
83._at:  Nu_std_lagged = .82
84._at:  Nu_std_lagged = .83
85._at:  Nu_std_lagged = .84
86._at:  Nu_std_lagged = .85
87._at:  Nu_std_lagged = .86
88._at:  Nu_std_lagged = .87
89._at:  Nu_std_lagged = .88
90._at:  Nu_std_lagged = .89
91._at:  Nu_std_lagged =  .9
92._at:  Nu_std_lagged = .91
93._at:  Nu_std_lagged = .92
94._at:  Nu_std_lagged = .93
95._at:  Nu_std_lagged = .94
96._at:  Nu_std_lagged = .95
97._at:  Nu_std_lagged = .96
98._at:  Nu_std_lagged = .97
99._at:  Nu_std_lagged = .98
100._at: Nu_std_lagged = .99
101._at: Nu_std_lagged =   1

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
support_USSR_lagged |
                _at |
                 1  |  -.0052457   .0804015    -0.07   0.948    -.1628297    .1523383
                 2  |  -.0048378   .0772801    -0.06   0.950     -.156304    .1466285
                 3  |  -.0044344   .0742154    -0.06   0.952     -.149894    .1410252
                 4  |  -.0040356   .0712077    -0.06   0.955    -.1436002     .135529
                 5  |  -.0036414   .0682574    -0.05   0.957    -.1374234    .1301407
                 6  |  -.0032517   .0653649    -0.05   0.960    -.1313645     .124861
                 7  |  -.0028667   .0625305    -0.05   0.963    -.1254243    .1196909
                 8  |  -.0024862    .059755    -0.04   0.967    -.1196038    .1146314
                 9  |  -.0021103   .0570388    -0.04   0.970    -.1139044    .1096838
                10  |   -.001739   .0543828    -0.03   0.974    -.1083272    .1048493
                11  |  -.0013723   .0517876    -0.03   0.979     -.102874    .1001295
                12  |  -.0010101   .0492542    -0.02   0.984    -.0975465    .0955263
                13  |  -.0006525   .0467836    -0.01   0.989    -.0923467    .0910416
                14  |  -.0002996   .0443771    -0.01   0.995    -.0872771     .086678
                15  |   .0000488   .0420361     0.00   0.999    -.0823404    .0824381
                16  |   .0003927   .0397622     0.01   0.992    -.0775399    .0783252
                17  |   .0007319   .0375574     0.02   0.984    -.0728794    .0743431
                18  |   .0010665    .035424     0.03   0.976    -.0683631    .0704962
                19  |   .0013966   .0333644     0.04   0.967    -.0639965    .0667897
                20  |   .0017221   .0313819     0.05   0.956    -.0597854    .0632296
                21  |    .002043   .0294801     0.07   0.945     -.055737     .059823
                22  |   .0023593   .0276633     0.09   0.932    -.0518596    .0565783
                23  |   .0026711   .0259363     0.10   0.918    -.0481631    .0535053
                24  |   .0029783    .024305     0.12   0.902    -.0446587    .0506152
                25  |   .0032808    .022776     0.14   0.885    -.0413594    .0479211
                26  |   .0035788    .021357     0.17   0.867    -.0382801    .0454378
                27  |   .0038722   .0200563     0.19   0.847    -.0354373    .0431818
                28  |   .0041611   .0188829     0.22   0.826    -.0328487    .0411708
                29  |   .0044453   .0178461     0.25   0.803    -.0305325    .0394231
                30  |    .004725    .016955     0.28   0.780    -.0285062    .0379562
                31  |   .0050001   .0162171     0.31   0.758    -.0267849    .0367851
                32  |   .0052706   .0156379     0.34   0.736     -.025379    .0359202
                33  |   .0055365    .015219     0.36   0.716    -.0242921    .0353652
                34  |   .0057979    .014958     0.39   0.698    -.0235192     .035115
                35  |   .0060546   .0148477     0.41   0.683    -.0230464    .0351556
                36  |   .0063068   .0148766     0.42   0.672    -.0228508    .0354644
                37  |   .0065544   .0150297     0.44   0.663    -.0229032    .0360121
                38  |   .0067974   .0152898     0.44   0.657    -.0231701     .036765
                39  |   .0070359   .0156392     0.45   0.653    -.0236165    .0376882
                40  |   .0072697   .0160604     0.45   0.651     -.024208    .0387475
                41  |    .007499    .016537     0.45   0.650    -.0249129    .0399109
                42  |   .0077237   .0170545     0.45   0.651    -.0257025    .0411498
                43  |   .0079438      .0176     0.45   0.652    -.0265516    .0424392
                44  |   .0081593   .0181627     0.45   0.653     -.027439    .0437576
                45  |   .0083702   .0187334     0.45   0.655    -.0283465     .045087
                46  |   .0085766   .0193042     0.44   0.657    -.0292589    .0464121
                47  |   .0087784   .0198688     0.44   0.659    -.0301638    .0477205
                48  |   .0089756    .020422     0.44   0.660    -.0310507    .0490019
                49  |   .0091682   .0209593     0.44   0.662    -.0319113    .0502476
                50  |   .0093562   .0214773     0.44   0.663    -.0327385     .051451
                51  |   .0095397   .0219732     0.43   0.664    -.0335269    .0526063
                52  |   .0097185   .0224446     0.43   0.665    -.0342721    .0537091
                53  |   .0098928   .0228898     0.43   0.666    -.0349704    .0547561
                54  |   .0100625   .0233075     0.43   0.666    -.0356194    .0557445
                55  |   .0102277   .0236967     0.43   0.666    -.0362171    .0566724
                56  |   .0103882   .0240567     0.43   0.666    -.0367621    .0575385
                57  |   .0105442   .0243871     0.43   0.665    -.0372537     .058342
                58  |   .0106955   .0246879     0.43   0.665    -.0376918    .0590828
                59  |   .0108423   .0249591     0.43   0.664    -.0380765    .0597612
                60  |   .0109845   .0252011     0.44   0.663    -.0384087    .0603778
                61  |   .0111222   .0254146     0.44   0.662    -.0386895    .0609338
                62  |   .0112552   .0256003     0.44   0.660    -.0389204    .0614308
                63  |   .0113837   .0257593     0.44   0.659    -.0391035    .0618709
                64  |   .0115076   .0258928     0.44   0.657    -.0392413    .0622565
                65  |   .0116269   .0260023     0.45   0.655    -.0393367    .0625904
                66  |   .0117416   .0260896     0.45   0.653     -.039393    .0628762
                67  |   .0118517   .0261565     0.45   0.650    -.0394141    .0631176
                68  |   .0119573   .0262054     0.46   0.648    -.0394044     .063319
                69  |   .0120583   .0262388     0.46   0.646    -.0393688    .0634853
                70  |   .0121547   .0262593     0.46   0.643    -.0393127     .063622
                71  |   .0122465   .0262702     0.47   0.641    -.0392421    .0637351
                72  |   .0123337   .0262747     0.47   0.639    -.0391638    .0638312
                73  |   .0124163   .0262766     0.47   0.637    -.0390849    .0639176
                74  |   .0124944     .02628     0.48   0.634    -.0390135    .0640023
                75  |   .0125679   .0262892     0.48   0.633     -.038958    .0640938
                76  |   .0126368   .0263089     0.48   0.631    -.0389276    .0642012
                77  |   .0127011    .026344     0.48   0.630    -.0389321    .0643344
                78  |   .0127609   .0263998     0.48   0.629    -.0389819    .0645036
                79  |    .012816   .0264819     0.48   0.628    -.0390876    .0647196
                80  |   .0128666   .0265959     0.48   0.629    -.0392605    .0649936
                81  |   .0129126   .0267477     0.48   0.629    -.0395119    .0653371
                82  |    .012954    .026943     0.48   0.631    -.0398534    .0657614
                83  |   .0129908   .0271878     0.48   0.633    -.0402963    .0662779
                84  |   .0130231   .0274877     0.47   0.636    -.0408518    .0668979
                85  |   .0130507   .0278481     0.47   0.639    -.0415305    .0676319
                86  |   .0130738   .0282741     0.46   0.644    -.0423424      .06849
                87  |   .0130923   .0287704     0.46   0.649    -.0432967    .0694813
                88  |   .0131062   .0293412     0.45   0.655    -.0444015    .0706139
                89  |   .0131156   .0299901     0.44   0.662    -.0456639     .071895
                90  |   .0131203     .03072     0.43   0.669    -.0470897    .0733304
                91  |   .0131205   .0315333     0.42   0.677    -.0486837    .0749247
                92  |   .0131161   .0324319     0.40   0.686    -.0504494    .0766815
                93  |   .0131071    .033417     0.39   0.695     -.052389    .0786031
                94  |   .0130935   .0344891     0.38   0.704    -.0545039    .0806909
                95  |   .0130754   .0356485     0.37   0.714    -.0567945    .0829452
                96  |   .0130526   .0368951     0.35   0.724    -.0592604    .0853656
                97  |   .0130253   .0382281     0.34   0.733    -.0619004     .087951
                98  |   .0129934   .0396469     0.33   0.743    -.0647131    .0906999
                99  |   .0129569   .0411503     0.31   0.753    -.0676962      .09361
               100  |   .0129159   .0427372     0.30   0.762    -.0708475    .0966792
               101  |   .0128702   .0444062     0.29   0.772    -.0741644    .0999048
-------------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) xtitle("Lagged Nuclear Latency") ytitle("Effects on Nuclear Latency") title("Marginal Effects o
> f USSR Signals of Support on Nuclear Latency") level(95)

Variables that uniquely identify margins: Nu_std_lagged

. * Figure A11
. 
. 
. esttab USSRp1 USSRp2 using TableA7.tex, se star(+ 0.10 * 0.05 ** 0.01) replace
(file TableA7.tex not found)
(output written to TableA7.tex)

. 
. 
. 
. log close
      name:  <unnamed>
       log:  C:\My Documents\PSRMLaptop\Hobby\Nuclear Latency\FPA_replication\Analysis_Appendix_log.log
  log type:  text
 closed on:   1 Nov 2024, 16:33:14
-------------------------------------------------------------------------------------------------------------------------------------------
